AmazonSageMaker (AWS SDK for Java

All Known Subinterfaces:

AmazonSageMakerAsync

All Known Implementing Classes:

AbstractAmazonSageMaker, AbstractAmazonSageMakerAsync, AmazonSageMakerAsyncClient, AmazonSageMakerClient
```
@Generated(value="com.amazonaws:aws-java-sdk-code-generator")
public interface AmazonSageMaker
```
Interface for accessing SageMaker.
Note: Do not directly implement this interface, new methods are added to it regularly. Extend from AbstractAmazonSageMaker instead.

Provides APIs for creating and managing SageMaker resources.

Other Resources:
- SageMaker Developer Guide
- Amazon Augmented AI Runtime API Reference

Field Summary

Fields
Modifier and Type Field and Description

static String ENDPOINT_PREFIX
The region metadata service name for computing region endpoints.

Fields
Modifier and Type	Field and Description
`static String`	`ENDPOINT_PREFIX` The region metadata service name for computing region endpoints.

Method Summary

All Methods Instance Methods Abstract Methods
Modifier and Type	Method and Description
`AddAssociationResult`	`addAssociation(AddAssociationRequest addAssociationRequest)` Creates an association between the source and the destination.
`AddTagsResult`	`addTags(AddTagsRequest addTagsRequest)` Adds or overwrites one or more tags for the specified SageMaker resource.
`AssociateTrialComponentResult`	`associateTrialComponent(AssociateTrialComponentRequest associateTrialComponentRequest)` Associates a trial component with a trial.
`BatchDescribeModelPackageResult`	`batchDescribeModelPackage(BatchDescribeModelPackageRequest batchDescribeModelPackageRequest)` This action batch describes a list of versioned model packages
`CreateActionResult`	`createAction(CreateActionRequest createActionRequest)` Creates an action.
`CreateAlgorithmResult`	`createAlgorithm(CreateAlgorithmRequest createAlgorithmRequest)` Create a machine learning algorithm that you can use in SageMaker and list in the Amazon Web Services Marketplace.
`CreateAppResult`	`createApp(CreateAppRequest createAppRequest)` Creates a running app for the specified UserProfile.
`CreateAppImageConfigResult`	`createAppImageConfig(CreateAppImageConfigRequest createAppImageConfigRequest)` Creates a configuration for running a SageMaker image as a KernelGateway app.
`CreateArtifactResult`	`createArtifact(CreateArtifactRequest createArtifactRequest)` Creates an artifact.
`CreateAutoMLJobResult`	`createAutoMLJob(CreateAutoMLJobRequest createAutoMLJobRequest)` Creates an Autopilot job also referred to as Autopilot experiment or AutoML job.
`CreateAutoMLJobV2Result`	`createAutoMLJobV2(CreateAutoMLJobV2Request createAutoMLJobV2Request)` Creates an Autopilot job also referred to as Autopilot experiment or AutoML job V2.
`CreateClusterResult`	`createCluster(CreateClusterRequest createClusterRequest)` Creates a SageMaker HyperPod cluster.
`CreateCodeRepositoryResult`	`createCodeRepository(CreateCodeRepositoryRequest createCodeRepositoryRequest)` Creates a Git repository as a resource in your SageMaker account.
`CreateCompilationJobResult`	`createCompilationJob(CreateCompilationJobRequest createCompilationJobRequest)` Starts a model compilation job.
`CreateContextResult`	`createContext(CreateContextRequest createContextRequest)` Creates a context.
`CreateDataQualityJobDefinitionResult`	`createDataQualityJobDefinition(CreateDataQualityJobDefinitionRequest createDataQualityJobDefinitionRequest)` Creates a definition for a job that monitors data quality and drift.
`CreateDeviceFleetResult`	`createDeviceFleet(CreateDeviceFleetRequest createDeviceFleetRequest)` Creates a device fleet.
`CreateDomainResult`	`createDomain(CreateDomainRequest createDomainRequest)` Creates a `Domain`.
`CreateEdgeDeploymentPlanResult`	`createEdgeDeploymentPlan(CreateEdgeDeploymentPlanRequest createEdgeDeploymentPlanRequest)` Creates an edge deployment plan, consisting of multiple stages.
`CreateEdgeDeploymentStageResult`	`createEdgeDeploymentStage(CreateEdgeDeploymentStageRequest createEdgeDeploymentStageRequest)` Creates a new stage in an existing edge deployment plan.
`CreateEdgePackagingJobResult`	`createEdgePackagingJob(CreateEdgePackagingJobRequest createEdgePackagingJobRequest)` Starts a SageMaker Edge Manager model packaging job.
`CreateEndpointResult`	`createEndpoint(CreateEndpointRequest createEndpointRequest)` Creates an endpoint using the endpoint configuration specified in the request.
`CreateEndpointConfigResult`	`createEndpointConfig(CreateEndpointConfigRequest createEndpointConfigRequest)` Creates an endpoint configuration that SageMaker hosting services uses to deploy models.
`CreateExperimentResult`	`createExperiment(CreateExperimentRequest createExperimentRequest)` Creates a SageMaker experiment.
`CreateFeatureGroupResult`	`createFeatureGroup(CreateFeatureGroupRequest createFeatureGroupRequest)` Create a new `FeatureGroup`.
`CreateFlowDefinitionResult`	`createFlowDefinition(CreateFlowDefinitionRequest createFlowDefinitionRequest)` Creates a flow definition.
`CreateHubResult`	`createHub(CreateHubRequest createHubRequest)` Create a hub.
`CreateHubContentReferenceResult`	`createHubContentReference(CreateHubContentReferenceRequest createHubContentReferenceRequest)` Create a hub content reference in order to add a model in the JumpStart public hub to a private hub.
`CreateHumanTaskUiResult`	`createHumanTaskUi(CreateHumanTaskUiRequest createHumanTaskUiRequest)` Defines the settings you will use for the human review workflow user interface.
`CreateHyperParameterTuningJobResult`	`createHyperParameterTuningJob(CreateHyperParameterTuningJobRequest createHyperParameterTuningJobRequest)` Starts a hyperparameter tuning job.
`CreateImageResult`	`createImage(CreateImageRequest createImageRequest)` Creates a custom SageMaker image.
`CreateImageVersionResult`	`createImageVersion(CreateImageVersionRequest createImageVersionRequest)` Creates a version of the SageMaker image specified by `ImageName`.
`CreateInferenceComponentResult`	`createInferenceComponent(CreateInferenceComponentRequest createInferenceComponentRequest)` Creates an inference component, which is a SageMaker hosting object that you can use to deploy a model to an endpoint.
`CreateInferenceExperimentResult`	`createInferenceExperiment(CreateInferenceExperimentRequest createInferenceExperimentRequest)` Creates an inference experiment using the configurations specified in the request.
`CreateInferenceRecommendationsJobResult`	`createInferenceRecommendationsJob(CreateInferenceRecommendationsJobRequest createInferenceRecommendationsJobRequest)` Starts a recommendation job.
`CreateLabelingJobResult`	`createLabelingJob(CreateLabelingJobRequest createLabelingJobRequest)` Creates a job that uses workers to label the data objects in your input dataset.
`CreateMlflowTrackingServerResult`	`createMlflowTrackingServer(CreateMlflowTrackingServerRequest createMlflowTrackingServerRequest)` Creates an MLflow Tracking Server using a general purpose Amazon S3 bucket as the artifact store.
`CreateModelResult`	`createModel(CreateModelRequest createModelRequest)` Creates a model in SageMaker.
`CreateModelBiasJobDefinitionResult`	`createModelBiasJobDefinition(CreateModelBiasJobDefinitionRequest createModelBiasJobDefinitionRequest)` Creates the definition for a model bias job.
`CreateModelCardResult`	`createModelCard(CreateModelCardRequest createModelCardRequest)` Creates an Amazon SageMaker Model Card.
`CreateModelCardExportJobResult`	`createModelCardExportJob(CreateModelCardExportJobRequest createModelCardExportJobRequest)` Creates an Amazon SageMaker Model Card export job.
`CreateModelExplainabilityJobDefinitionResult`	`createModelExplainabilityJobDefinition(CreateModelExplainabilityJobDefinitionRequest createModelExplainabilityJobDefinitionRequest)` Creates the definition for a model explainability job.
`CreateModelPackageResult`	`createModelPackage(CreateModelPackageRequest createModelPackageRequest)` Creates a model package that you can use to create SageMaker models or list on Amazon Web Services Marketplace, or a versioned model that is part of a model group.
`CreateModelPackageGroupResult`	`createModelPackageGroup(CreateModelPackageGroupRequest createModelPackageGroupRequest)` Creates a model group.
`CreateModelQualityJobDefinitionResult`	`createModelQualityJobDefinition(CreateModelQualityJobDefinitionRequest createModelQualityJobDefinitionRequest)` Creates a definition for a job that monitors model quality and drift.
`CreateMonitoringScheduleResult`	`createMonitoringSchedule(CreateMonitoringScheduleRequest createMonitoringScheduleRequest)` Creates a schedule that regularly starts Amazon SageMaker Processing Jobs to monitor the data captured for an Amazon SageMaker Endpoint.
`CreateNotebookInstanceResult`	`createNotebookInstance(CreateNotebookInstanceRequest createNotebookInstanceRequest)` Creates an SageMaker notebook instance.
`CreateNotebookInstanceLifecycleConfigResult`	`createNotebookInstanceLifecycleConfig(CreateNotebookInstanceLifecycleConfigRequest createNotebookInstanceLifecycleConfigRequest)` Creates a lifecycle configuration that you can associate with a notebook instance.
`CreateOptimizationJobResult`	`createOptimizationJob(CreateOptimizationJobRequest createOptimizationJobRequest)` Creates a job that optimizes a model for inference performance.
`CreatePipelineResult`	`createPipeline(CreatePipelineRequest createPipelineRequest)` Creates a pipeline using a JSON pipeline definition.
`CreatePresignedDomainUrlResult`	`createPresignedDomainUrl(CreatePresignedDomainUrlRequest createPresignedDomainUrlRequest)` Creates a URL for a specified UserProfile in a Domain.
`CreatePresignedMlflowTrackingServerUrlResult`	`createPresignedMlflowTrackingServerUrl(CreatePresignedMlflowTrackingServerUrlRequest createPresignedMlflowTrackingServerUrlRequest)` Returns a presigned URL that you can use to connect to the MLflow UI attached to your tracking server.
`CreatePresignedNotebookInstanceUrlResult`	`createPresignedNotebookInstanceUrl(CreatePresignedNotebookInstanceUrlRequest createPresignedNotebookInstanceUrlRequest)` Returns a URL that you can use to connect to the Jupyter server from a notebook instance.
`CreateProcessingJobResult`	`createProcessingJob(CreateProcessingJobRequest createProcessingJobRequest)` Creates a processing job.
`CreateProjectResult`	`createProject(CreateProjectRequest createProjectRequest)` Creates a machine learning (ML) project that can contain one or more templates that set up an ML pipeline from training to deploying an approved model.
`CreateSpaceResult`	`createSpace(CreateSpaceRequest createSpaceRequest)` Creates a private space or a space used for real time collaboration in a domain.
`CreateStudioLifecycleConfigResult`	`createStudioLifecycleConfig(CreateStudioLifecycleConfigRequest createStudioLifecycleConfigRequest)` Creates a new Amazon SageMaker Studio Lifecycle Configuration.
`CreateTrainingJobResult`	`createTrainingJob(CreateTrainingJobRequest createTrainingJobRequest)` Starts a model training job.
`CreateTransformJobResult`	`createTransformJob(CreateTransformJobRequest createTransformJobRequest)` Starts a transform job.
`CreateTrialResult`	`createTrial(CreateTrialRequest createTrialRequest)` Creates an SageMaker trial.
`CreateTrialComponentResult`	`createTrialComponent(CreateTrialComponentRequest createTrialComponentRequest)` Creates a trial component, which is a stage of a machine learning trial.
`CreateUserProfileResult`	`createUserProfile(CreateUserProfileRequest createUserProfileRequest)` Creates a user profile.
`CreateWorkforceResult`	`createWorkforce(CreateWorkforceRequest createWorkforceRequest)` Use this operation to create a workforce.
`CreateWorkteamResult`	`createWorkteam(CreateWorkteamRequest createWorkteamRequest)` Creates a new work team for labeling your data.
`DeleteActionResult`	`deleteAction(DeleteActionRequest deleteActionRequest)` Deletes an action.
`DeleteAlgorithmResult`	`deleteAlgorithm(DeleteAlgorithmRequest deleteAlgorithmRequest)` Removes the specified algorithm from your account.
`DeleteAppResult`	`deleteApp(DeleteAppRequest deleteAppRequest)` Used to stop and delete an app.
`DeleteAppImageConfigResult`	`deleteAppImageConfig(DeleteAppImageConfigRequest deleteAppImageConfigRequest)` Deletes an AppImageConfig.
`DeleteArtifactResult`	`deleteArtifact(DeleteArtifactRequest deleteArtifactRequest)` Deletes an artifact.
`DeleteAssociationResult`	`deleteAssociation(DeleteAssociationRequest deleteAssociationRequest)` Deletes an association.
`DeleteClusterResult`	`deleteCluster(DeleteClusterRequest deleteClusterRequest)` Delete a SageMaker HyperPod cluster.
`DeleteCodeRepositoryResult`	`deleteCodeRepository(DeleteCodeRepositoryRequest deleteCodeRepositoryRequest)` Deletes the specified Git repository from your account.
`DeleteCompilationJobResult`	`deleteCompilationJob(DeleteCompilationJobRequest deleteCompilationJobRequest)` Deletes the specified compilation job.
`DeleteContextResult`	`deleteContext(DeleteContextRequest deleteContextRequest)` Deletes an context.
`DeleteDataQualityJobDefinitionResult`	`deleteDataQualityJobDefinition(DeleteDataQualityJobDefinitionRequest deleteDataQualityJobDefinitionRequest)` Deletes a data quality monitoring job definition.
`DeleteDeviceFleetResult`	`deleteDeviceFleet(DeleteDeviceFleetRequest deleteDeviceFleetRequest)` Deletes a fleet.
`DeleteDomainResult`	`deleteDomain(DeleteDomainRequest deleteDomainRequest)` Used to delete a domain.
`DeleteEdgeDeploymentPlanResult`	`deleteEdgeDeploymentPlan(DeleteEdgeDeploymentPlanRequest deleteEdgeDeploymentPlanRequest)` Deletes an edge deployment plan if (and only if) all the stages in the plan are inactive or there are no stages in the plan.
`DeleteEdgeDeploymentStageResult`	`deleteEdgeDeploymentStage(DeleteEdgeDeploymentStageRequest deleteEdgeDeploymentStageRequest)` Delete a stage in an edge deployment plan if (and only if) the stage is inactive.
`DeleteEndpointResult`	`deleteEndpoint(DeleteEndpointRequest deleteEndpointRequest)` Deletes an endpoint.
`DeleteEndpointConfigResult`	`deleteEndpointConfig(DeleteEndpointConfigRequest deleteEndpointConfigRequest)` Deletes an endpoint configuration.
`DeleteExperimentResult`	`deleteExperiment(DeleteExperimentRequest deleteExperimentRequest)` Deletes an SageMaker experiment.
`DeleteFeatureGroupResult`	`deleteFeatureGroup(DeleteFeatureGroupRequest deleteFeatureGroupRequest)` Delete the `FeatureGroup` and any data that was written to the `OnlineStore` of the `FeatureGroup`.
`DeleteFlowDefinitionResult`	`deleteFlowDefinition(DeleteFlowDefinitionRequest deleteFlowDefinitionRequest)` Deletes the specified flow definition.
`DeleteHubResult`	`deleteHub(DeleteHubRequest deleteHubRequest)` Delete a hub.
`DeleteHubContentResult`	`deleteHubContent(DeleteHubContentRequest deleteHubContentRequest)` Delete the contents of a hub.
`DeleteHubContentReferenceResult`	`deleteHubContentReference(DeleteHubContentReferenceRequest deleteHubContentReferenceRequest)` Delete a hub content reference in order to remove a model from a private hub.
`DeleteHumanTaskUiResult`	`deleteHumanTaskUi(DeleteHumanTaskUiRequest deleteHumanTaskUiRequest)` Use this operation to delete a human task user interface (worker task template).
`DeleteHyperParameterTuningJobResult`	`deleteHyperParameterTuningJob(DeleteHyperParameterTuningJobRequest deleteHyperParameterTuningJobRequest)` Deletes a hyperparameter tuning job.
`DeleteImageResult`	`deleteImage(DeleteImageRequest deleteImageRequest)` Deletes a SageMaker image and all versions of the image.
`DeleteImageVersionResult`	`deleteImageVersion(DeleteImageVersionRequest deleteImageVersionRequest)` Deletes a version of a SageMaker image.
`DeleteInferenceComponentResult`	`deleteInferenceComponent(DeleteInferenceComponentRequest deleteInferenceComponentRequest)` Deletes an inference component.
`DeleteInferenceExperimentResult`	`deleteInferenceExperiment(DeleteInferenceExperimentRequest deleteInferenceExperimentRequest)` Deletes an inference experiment.
`DeleteMlflowTrackingServerResult`	`deleteMlflowTrackingServer(DeleteMlflowTrackingServerRequest deleteMlflowTrackingServerRequest)` Deletes an MLflow Tracking Server.
`DeleteModelResult`	`deleteModel(DeleteModelRequest deleteModelRequest)` Deletes a model.
`DeleteModelBiasJobDefinitionResult`	`deleteModelBiasJobDefinition(DeleteModelBiasJobDefinitionRequest deleteModelBiasJobDefinitionRequest)` Deletes an Amazon SageMaker model bias job definition.
`DeleteModelCardResult`	`deleteModelCard(DeleteModelCardRequest deleteModelCardRequest)` Deletes an Amazon SageMaker Model Card.
`DeleteModelExplainabilityJobDefinitionResult`	`deleteModelExplainabilityJobDefinition(DeleteModelExplainabilityJobDefinitionRequest deleteModelExplainabilityJobDefinitionRequest)` Deletes an Amazon SageMaker model explainability job definition.
`DeleteModelPackageResult`	`deleteModelPackage(DeleteModelPackageRequest deleteModelPackageRequest)` Deletes a model package.
`DeleteModelPackageGroupResult`	`deleteModelPackageGroup(DeleteModelPackageGroupRequest deleteModelPackageGroupRequest)` Deletes the specified model group.
`DeleteModelPackageGroupPolicyResult`	`deleteModelPackageGroupPolicy(DeleteModelPackageGroupPolicyRequest deleteModelPackageGroupPolicyRequest)` Deletes a model group resource policy.
`DeleteModelQualityJobDefinitionResult`	`deleteModelQualityJobDefinition(DeleteModelQualityJobDefinitionRequest deleteModelQualityJobDefinitionRequest)` Deletes the secified model quality monitoring job definition.
`DeleteMonitoringScheduleResult`	`deleteMonitoringSchedule(DeleteMonitoringScheduleRequest deleteMonitoringScheduleRequest)` Deletes a monitoring schedule.
`DeleteNotebookInstanceResult`	`deleteNotebookInstance(DeleteNotebookInstanceRequest deleteNotebookInstanceRequest)` Deletes an SageMaker notebook instance.
`DeleteNotebookInstanceLifecycleConfigResult`	`deleteNotebookInstanceLifecycleConfig(DeleteNotebookInstanceLifecycleConfigRequest deleteNotebookInstanceLifecycleConfigRequest)` Deletes a notebook instance lifecycle configuration.
`DeleteOptimizationJobResult`	`deleteOptimizationJob(DeleteOptimizationJobRequest deleteOptimizationJobRequest)` Deletes an optimization job.
`DeletePipelineResult`	`deletePipeline(DeletePipelineRequest deletePipelineRequest)` Deletes a pipeline if there are no running instances of the pipeline.
`DeleteProjectResult`	`deleteProject(DeleteProjectRequest deleteProjectRequest)` Delete the specified project.
`DeleteSpaceResult`	`deleteSpace(DeleteSpaceRequest deleteSpaceRequest)` Used to delete a space.
`DeleteStudioLifecycleConfigResult`	`deleteStudioLifecycleConfig(DeleteStudioLifecycleConfigRequest deleteStudioLifecycleConfigRequest)` Deletes the Amazon SageMaker Studio Lifecycle Configuration.
`DeleteTagsResult`	`deleteTags(DeleteTagsRequest deleteTagsRequest)` Deletes the specified tags from an SageMaker resource.
`DeleteTrialResult`	`deleteTrial(DeleteTrialRequest deleteTrialRequest)` Deletes the specified trial.
`DeleteTrialComponentResult`	`deleteTrialComponent(DeleteTrialComponentRequest deleteTrialComponentRequest)` Deletes the specified trial component.
`DeleteUserProfileResult`	`deleteUserProfile(DeleteUserProfileRequest deleteUserProfileRequest)` Deletes a user profile.
`DeleteWorkforceResult`	`deleteWorkforce(DeleteWorkforceRequest deleteWorkforceRequest)` Use this operation to delete a workforce.
`DeleteWorkteamResult`	`deleteWorkteam(DeleteWorkteamRequest deleteWorkteamRequest)` Deletes an existing work team.
`DeregisterDevicesResult`	`deregisterDevices(DeregisterDevicesRequest deregisterDevicesRequest)` Deregisters the specified devices.
`DescribeActionResult`	`describeAction(DescribeActionRequest describeActionRequest)` Describes an action.
`DescribeAlgorithmResult`	`describeAlgorithm(DescribeAlgorithmRequest describeAlgorithmRequest)` Returns a description of the specified algorithm that is in your account.
`DescribeAppResult`	`describeApp(DescribeAppRequest describeAppRequest)` Describes the app.
`DescribeAppImageConfigResult`	`describeAppImageConfig(DescribeAppImageConfigRequest describeAppImageConfigRequest)` Describes an AppImageConfig.
`DescribeArtifactResult`	`describeArtifact(DescribeArtifactRequest describeArtifactRequest)` Describes an artifact.
`DescribeAutoMLJobResult`	`describeAutoMLJob(DescribeAutoMLJobRequest describeAutoMLJobRequest)` Returns information about an AutoML job created by calling CreateAutoMLJob.
`DescribeAutoMLJobV2Result`	`describeAutoMLJobV2(DescribeAutoMLJobV2Request describeAutoMLJobV2Request)` Returns information about an AutoML job created by calling CreateAutoMLJobV2 or CreateAutoMLJob.
`DescribeClusterResult`	`describeCluster(DescribeClusterRequest describeClusterRequest)` Retrieves information of a SageMaker HyperPod cluster.
`DescribeClusterNodeResult`	`describeClusterNode(DescribeClusterNodeRequest describeClusterNodeRequest)` Retrieves information of a node (also called a instance interchangeably) of a SageMaker HyperPod cluster.
`DescribeCodeRepositoryResult`	`describeCodeRepository(DescribeCodeRepositoryRequest describeCodeRepositoryRequest)` Gets details about the specified Git repository.
`DescribeCompilationJobResult`	`describeCompilationJob(DescribeCompilationJobRequest describeCompilationJobRequest)` Returns information about a model compilation job.
`DescribeContextResult`	`describeContext(DescribeContextRequest describeContextRequest)` Describes a context.
`DescribeDataQualityJobDefinitionResult`	`describeDataQualityJobDefinition(DescribeDataQualityJobDefinitionRequest describeDataQualityJobDefinitionRequest)` Gets the details of a data quality monitoring job definition.
`DescribeDeviceResult`	`describeDevice(DescribeDeviceRequest describeDeviceRequest)` Describes the device.
`DescribeDeviceFleetResult`	`describeDeviceFleet(DescribeDeviceFleetRequest describeDeviceFleetRequest)` A description of the fleet the device belongs to.
`DescribeDomainResult`	`describeDomain(DescribeDomainRequest describeDomainRequest)` The description of the domain.
`DescribeEdgeDeploymentPlanResult`	`describeEdgeDeploymentPlan(DescribeEdgeDeploymentPlanRequest describeEdgeDeploymentPlanRequest)` Describes an edge deployment plan with deployment status per stage.
`DescribeEdgePackagingJobResult`	`describeEdgePackagingJob(DescribeEdgePackagingJobRequest describeEdgePackagingJobRequest)` A description of edge packaging jobs.
`DescribeEndpointResult`	`describeEndpoint(DescribeEndpointRequest describeEndpointRequest)` Returns the description of an endpoint.
`DescribeEndpointConfigResult`	`describeEndpointConfig(DescribeEndpointConfigRequest describeEndpointConfigRequest)` Returns the description of an endpoint configuration created using the `CreateEndpointConfig` API.
`DescribeExperimentResult`	`describeExperiment(DescribeExperimentRequest describeExperimentRequest)` Provides a list of an experiment's properties.
`DescribeFeatureGroupResult`	`describeFeatureGroup(DescribeFeatureGroupRequest describeFeatureGroupRequest)` Use this operation to describe a `FeatureGroup`.
`DescribeFeatureMetadataResult`	`describeFeatureMetadata(DescribeFeatureMetadataRequest describeFeatureMetadataRequest)` Shows the metadata for a feature within a feature group.
`DescribeFlowDefinitionResult`	`describeFlowDefinition(DescribeFlowDefinitionRequest describeFlowDefinitionRequest)` Returns information about the specified flow definition.
`DescribeHubResult`	`describeHub(DescribeHubRequest describeHubRequest)` Describes a hub.
`DescribeHubContentResult`	`describeHubContent(DescribeHubContentRequest describeHubContentRequest)` Describe the content of a hub.
`DescribeHumanTaskUiResult`	`describeHumanTaskUi(DescribeHumanTaskUiRequest describeHumanTaskUiRequest)` Returns information about the requested human task user interface (worker task template).
`DescribeHyperParameterTuningJobResult`	`describeHyperParameterTuningJob(DescribeHyperParameterTuningJobRequest describeHyperParameterTuningJobRequest)` Returns a description of a hyperparameter tuning job, depending on the fields selected.
`DescribeImageResult`	`describeImage(DescribeImageRequest describeImageRequest)` Describes a SageMaker image.
`DescribeImageVersionResult`	`describeImageVersion(DescribeImageVersionRequest describeImageVersionRequest)` Describes a version of a SageMaker image.
`DescribeInferenceComponentResult`	`describeInferenceComponent(DescribeInferenceComponentRequest describeInferenceComponentRequest)` Returns information about an inference component.
`DescribeInferenceExperimentResult`	`describeInferenceExperiment(DescribeInferenceExperimentRequest describeInferenceExperimentRequest)` Returns details about an inference experiment.
`DescribeInferenceRecommendationsJobResult`	`describeInferenceRecommendationsJob(DescribeInferenceRecommendationsJobRequest describeInferenceRecommendationsJobRequest)` Provides the results of the Inference Recommender job.
`DescribeLabelingJobResult`	`describeLabelingJob(DescribeLabelingJobRequest describeLabelingJobRequest)` Gets information about a labeling job.
`DescribeLineageGroupResult`	`describeLineageGroup(DescribeLineageGroupRequest describeLineageGroupRequest)` Provides a list of properties for the requested lineage group.
`DescribeMlflowTrackingServerResult`	`describeMlflowTrackingServer(DescribeMlflowTrackingServerRequest describeMlflowTrackingServerRequest)` Returns information about an MLflow Tracking Server.
`DescribeModelResult`	`describeModel(DescribeModelRequest describeModelRequest)` Describes a model that you created using the `CreateModel` API.
`DescribeModelBiasJobDefinitionResult`	`describeModelBiasJobDefinition(DescribeModelBiasJobDefinitionRequest describeModelBiasJobDefinitionRequest)` Returns a description of a model bias job definition.
`DescribeModelCardResult`	`describeModelCard(DescribeModelCardRequest describeModelCardRequest)` Describes the content, creation time, and security configuration of an Amazon SageMaker Model Card.
`DescribeModelCardExportJobResult`	`describeModelCardExportJob(DescribeModelCardExportJobRequest describeModelCardExportJobRequest)` Describes an Amazon SageMaker Model Card export job.
`DescribeModelExplainabilityJobDefinitionResult`	`describeModelExplainabilityJobDefinition(DescribeModelExplainabilityJobDefinitionRequest describeModelExplainabilityJobDefinitionRequest)` Returns a description of a model explainability job definition.
`DescribeModelPackageResult`	`describeModelPackage(DescribeModelPackageRequest describeModelPackageRequest)` Returns a description of the specified model package, which is used to create SageMaker models or list them on Amazon Web Services Marketplace.
`DescribeModelPackageGroupResult`	`describeModelPackageGroup(DescribeModelPackageGroupRequest describeModelPackageGroupRequest)` Gets a description for the specified model group.
`DescribeModelQualityJobDefinitionResult`	`describeModelQualityJobDefinition(DescribeModelQualityJobDefinitionRequest describeModelQualityJobDefinitionRequest)` Returns a description of a model quality job definition.
`DescribeMonitoringScheduleResult`	`describeMonitoringSchedule(DescribeMonitoringScheduleRequest describeMonitoringScheduleRequest)` Describes the schedule for a monitoring job.
`DescribeNotebookInstanceResult`	`describeNotebookInstance(DescribeNotebookInstanceRequest describeNotebookInstanceRequest)` Returns information about a notebook instance.
`DescribeNotebookInstanceLifecycleConfigResult`	`describeNotebookInstanceLifecycleConfig(DescribeNotebookInstanceLifecycleConfigRequest describeNotebookInstanceLifecycleConfigRequest)` Returns a description of a notebook instance lifecycle configuration.
`DescribeOptimizationJobResult`	`describeOptimizationJob(DescribeOptimizationJobRequest describeOptimizationJobRequest)` Provides the properties of the specified optimization job.
`DescribePipelineResult`	`describePipeline(DescribePipelineRequest describePipelineRequest)` Describes the details of a pipeline.
`DescribePipelineDefinitionForExecutionResult`	`describePipelineDefinitionForExecution(DescribePipelineDefinitionForExecutionRequest describePipelineDefinitionForExecutionRequest)` Describes the details of an execution's pipeline definition.
`DescribePipelineExecutionResult`	`describePipelineExecution(DescribePipelineExecutionRequest describePipelineExecutionRequest)` Describes the details of a pipeline execution.
`DescribeProcessingJobResult`	`describeProcessingJob(DescribeProcessingJobRequest describeProcessingJobRequest)` Returns a description of a processing job.
`DescribeProjectResult`	`describeProject(DescribeProjectRequest describeProjectRequest)` Describes the details of a project.
`DescribeSpaceResult`	`describeSpace(DescribeSpaceRequest describeSpaceRequest)` Describes the space.
`DescribeStudioLifecycleConfigResult`	`describeStudioLifecycleConfig(DescribeStudioLifecycleConfigRequest describeStudioLifecycleConfigRequest)` Describes the Amazon SageMaker Studio Lifecycle Configuration.
`DescribeSubscribedWorkteamResult`	`describeSubscribedWorkteam(DescribeSubscribedWorkteamRequest describeSubscribedWorkteamRequest)` Gets information about a work team provided by a vendor.
`DescribeTrainingJobResult`	`describeTrainingJob(DescribeTrainingJobRequest describeTrainingJobRequest)` Returns information about a training job.
`DescribeTransformJobResult`	`describeTransformJob(DescribeTransformJobRequest describeTransformJobRequest)` Returns information about a transform job.
`DescribeTrialResult`	`describeTrial(DescribeTrialRequest describeTrialRequest)` Provides a list of a trial's properties.
`DescribeTrialComponentResult`	`describeTrialComponent(DescribeTrialComponentRequest describeTrialComponentRequest)` Provides a list of a trials component's properties.
`DescribeUserProfileResult`	`describeUserProfile(DescribeUserProfileRequest describeUserProfileRequest)` Describes a user profile.
`DescribeWorkforceResult`	`describeWorkforce(DescribeWorkforceRequest describeWorkforceRequest)` Lists private workforce information, including workforce name, Amazon Resource Name (ARN), and, if applicable, allowed IP address ranges (CIDRs).
`DescribeWorkteamResult`	`describeWorkteam(DescribeWorkteamRequest describeWorkteamRequest)` Gets information about a specific work team.
`DisableSagemakerServicecatalogPortfolioResult`	`disableSagemakerServicecatalogPortfolio(DisableSagemakerServicecatalogPortfolioRequest disableSagemakerServicecatalogPortfolioRequest)` Disables using Service Catalog in SageMaker.
`DisassociateTrialComponentResult`	`disassociateTrialComponent(DisassociateTrialComponentRequest disassociateTrialComponentRequest)` Disassociates a trial component from a trial.
`EnableSagemakerServicecatalogPortfolioResult`	`enableSagemakerServicecatalogPortfolio(EnableSagemakerServicecatalogPortfolioRequest enableSagemakerServicecatalogPortfolioRequest)` Enables using Service Catalog in SageMaker.
`ResponseMetadata`	`getCachedResponseMetadata(AmazonWebServiceRequest request)` Returns additional metadata for a previously executed successful request, typically used for debugging issues where a service isn't acting as expected.
`GetDeviceFleetReportResult`	`getDeviceFleetReport(GetDeviceFleetReportRequest getDeviceFleetReportRequest)` Describes a fleet.
`GetLineageGroupPolicyResult`	`getLineageGroupPolicy(GetLineageGroupPolicyRequest getLineageGroupPolicyRequest)` The resource policy for the lineage group.
`GetModelPackageGroupPolicyResult`	`getModelPackageGroupPolicy(GetModelPackageGroupPolicyRequest getModelPackageGroupPolicyRequest)` Gets a resource policy that manages access for a model group.
`GetSagemakerServicecatalogPortfolioStatusResult`	`getSagemakerServicecatalogPortfolioStatus(GetSagemakerServicecatalogPortfolioStatusRequest getSagemakerServicecatalogPortfolioStatusRequest)` Gets the status of Service Catalog in SageMaker.
`GetScalingConfigurationRecommendationResult`	`getScalingConfigurationRecommendation(GetScalingConfigurationRecommendationRequest getScalingConfigurationRecommendationRequest)` Starts an Amazon SageMaker Inference Recommender autoscaling recommendation job.
`GetSearchSuggestionsResult`	`getSearchSuggestions(GetSearchSuggestionsRequest getSearchSuggestionsRequest)` An auto-complete API for the search functionality in the SageMaker console.
`ImportHubContentResult`	`importHubContent(ImportHubContentRequest importHubContentRequest)` Import hub content.
`ListActionsResult`	`listActions(ListActionsRequest listActionsRequest)` Lists the actions in your account and their properties.
`ListAlgorithmsResult`	`listAlgorithms(ListAlgorithmsRequest listAlgorithmsRequest)` Lists the machine learning algorithms that have been created.
`ListAliasesResult`	`listAliases(ListAliasesRequest listAliasesRequest)` Lists the aliases of a specified image or image version.
`ListAppImageConfigsResult`	`listAppImageConfigs(ListAppImageConfigsRequest listAppImageConfigsRequest)` Lists the AppImageConfigs in your account and their properties.
`ListAppsResult`	`listApps(ListAppsRequest listAppsRequest)` Lists apps.
`ListArtifactsResult`	`listArtifacts(ListArtifactsRequest listArtifactsRequest)` Lists the artifacts in your account and their properties.
`ListAssociationsResult`	`listAssociations(ListAssociationsRequest listAssociationsRequest)` Lists the associations in your account and their properties.
`ListAutoMLJobsResult`	`listAutoMLJobs(ListAutoMLJobsRequest listAutoMLJobsRequest)` Request a list of jobs.
`ListCandidatesForAutoMLJobResult`	`listCandidatesForAutoMLJob(ListCandidatesForAutoMLJobRequest listCandidatesForAutoMLJobRequest)` List the candidates created for the job.
`ListClusterNodesResult`	`listClusterNodes(ListClusterNodesRequest listClusterNodesRequest)` Retrieves the list of instances (also called nodes interchangeably) in a SageMaker HyperPod cluster.
`ListClustersResult`	`listClusters(ListClustersRequest listClustersRequest)` Retrieves the list of SageMaker HyperPod clusters.
`ListCodeRepositoriesResult`	`listCodeRepositories(ListCodeRepositoriesRequest listCodeRepositoriesRequest)` Gets a list of the Git repositories in your account.
`ListCompilationJobsResult`	`listCompilationJobs(ListCompilationJobsRequest listCompilationJobsRequest)` Lists model compilation jobs that satisfy various filters.
`ListContextsResult`	`listContexts(ListContextsRequest listContextsRequest)` Lists the contexts in your account and their properties.
`ListDataQualityJobDefinitionsResult`	`listDataQualityJobDefinitions(ListDataQualityJobDefinitionsRequest listDataQualityJobDefinitionsRequest)` Lists the data quality job definitions in your account.
`ListDeviceFleetsResult`	`listDeviceFleets(ListDeviceFleetsRequest listDeviceFleetsRequest)` Returns a list of devices in the fleet.
`ListDevicesResult`	`listDevices(ListDevicesRequest listDevicesRequest)` A list of devices.
`ListDomainsResult`	`listDomains(ListDomainsRequest listDomainsRequest)` Lists the domains.
`ListEdgeDeploymentPlansResult`	`listEdgeDeploymentPlans(ListEdgeDeploymentPlansRequest listEdgeDeploymentPlansRequest)` Lists all edge deployment plans.
`ListEdgePackagingJobsResult`	`listEdgePackagingJobs(ListEdgePackagingJobsRequest listEdgePackagingJobsRequest)` Returns a list of edge packaging jobs.
`ListEndpointConfigsResult`	`listEndpointConfigs(ListEndpointConfigsRequest listEndpointConfigsRequest)` Lists endpoint configurations.
`ListEndpointsResult`	`listEndpoints(ListEndpointsRequest listEndpointsRequest)` Lists endpoints.
`ListExperimentsResult`	`listExperiments(ListExperimentsRequest listExperimentsRequest)` Lists all the experiments in your account.
`ListFeatureGroupsResult`	`listFeatureGroups(ListFeatureGroupsRequest listFeatureGroupsRequest)` List `FeatureGroup`s based on given filter and order.
`ListFlowDefinitionsResult`	`listFlowDefinitions(ListFlowDefinitionsRequest listFlowDefinitionsRequest)` Returns information about the flow definitions in your account.
`ListHubContentsResult`	`listHubContents(ListHubContentsRequest listHubContentsRequest)` List the contents of a hub.
`ListHubContentVersionsResult`	`listHubContentVersions(ListHubContentVersionsRequest listHubContentVersionsRequest)` List hub content versions.
`ListHubsResult`	`listHubs(ListHubsRequest listHubsRequest)` List all existing hubs.
`ListHumanTaskUisResult`	`listHumanTaskUis(ListHumanTaskUisRequest listHumanTaskUisRequest)` Returns information about the human task user interfaces in your account.
`ListHyperParameterTuningJobsResult`	`listHyperParameterTuningJobs(ListHyperParameterTuningJobsRequest listHyperParameterTuningJobsRequest)` Gets a list of HyperParameterTuningJobSummary objects that describe the hyperparameter tuning jobs launched in your account.
`ListImagesResult`	`listImages(ListImagesRequest listImagesRequest)` Lists the images in your account and their properties.
`ListImageVersionsResult`	`listImageVersions(ListImageVersionsRequest listImageVersionsRequest)` Lists the versions of a specified image and their properties.
`ListInferenceComponentsResult`	`listInferenceComponents(ListInferenceComponentsRequest listInferenceComponentsRequest)` Lists the inference components in your account and their properties.
`ListInferenceExperimentsResult`	`listInferenceExperiments(ListInferenceExperimentsRequest listInferenceExperimentsRequest)` Returns the list of all inference experiments.
`ListInferenceRecommendationsJobsResult`	`listInferenceRecommendationsJobs(ListInferenceRecommendationsJobsRequest listInferenceRecommendationsJobsRequest)` Lists recommendation jobs that satisfy various filters.
`ListInferenceRecommendationsJobStepsResult`	`listInferenceRecommendationsJobSteps(ListInferenceRecommendationsJobStepsRequest listInferenceRecommendationsJobStepsRequest)` Returns a list of the subtasks for an Inference Recommender job.
`ListLabelingJobsResult`	`listLabelingJobs(ListLabelingJobsRequest listLabelingJobsRequest)` Gets a list of labeling jobs.
`ListLabelingJobsForWorkteamResult`	`listLabelingJobsForWorkteam(ListLabelingJobsForWorkteamRequest listLabelingJobsForWorkteamRequest)` Gets a list of labeling jobs assigned to a specified work team.
`ListLineageGroupsResult`	`listLineageGroups(ListLineageGroupsRequest listLineageGroupsRequest)` A list of lineage groups shared with your Amazon Web Services account.
`ListMlflowTrackingServersResult`	`listMlflowTrackingServers(ListMlflowTrackingServersRequest listMlflowTrackingServersRequest)` Lists all MLflow Tracking Servers.
`ListModelBiasJobDefinitionsResult`	`listModelBiasJobDefinitions(ListModelBiasJobDefinitionsRequest listModelBiasJobDefinitionsRequest)` Lists model bias jobs definitions that satisfy various filters.
`ListModelCardExportJobsResult`	`listModelCardExportJobs(ListModelCardExportJobsRequest listModelCardExportJobsRequest)` List the export jobs for the Amazon SageMaker Model Card.
`ListModelCardsResult`	`listModelCards(ListModelCardsRequest listModelCardsRequest)` List existing model cards.
`ListModelCardVersionsResult`	`listModelCardVersions(ListModelCardVersionsRequest listModelCardVersionsRequest)` List existing versions of an Amazon SageMaker Model Card.
`ListModelExplainabilityJobDefinitionsResult`	`listModelExplainabilityJobDefinitions(ListModelExplainabilityJobDefinitionsRequest listModelExplainabilityJobDefinitionsRequest)` Lists model explainability job definitions that satisfy various filters.
`ListModelMetadataResult`	`listModelMetadata(ListModelMetadataRequest listModelMetadataRequest)` Lists the domain, framework, task, and model name of standard machine learning models found in common model zoos.
`ListModelPackageGroupsResult`	`listModelPackageGroups(ListModelPackageGroupsRequest listModelPackageGroupsRequest)` Gets a list of the model groups in your Amazon Web Services account.
`ListModelPackagesResult`	`listModelPackages(ListModelPackagesRequest listModelPackagesRequest)` Lists the model packages that have been created.
`ListModelQualityJobDefinitionsResult`	`listModelQualityJobDefinitions(ListModelQualityJobDefinitionsRequest listModelQualityJobDefinitionsRequest)` Gets a list of model quality monitoring job definitions in your account.
`ListModelsResult`	`listModels(ListModelsRequest listModelsRequest)` Lists models created with the `CreateModel` API.
`ListMonitoringAlertHistoryResult`	`listMonitoringAlertHistory(ListMonitoringAlertHistoryRequest listMonitoringAlertHistoryRequest)` Gets a list of past alerts in a model monitoring schedule.
`ListMonitoringAlertsResult`	`listMonitoringAlerts(ListMonitoringAlertsRequest listMonitoringAlertsRequest)` Gets the alerts for a single monitoring schedule.
`ListMonitoringExecutionsResult`	`listMonitoringExecutions(ListMonitoringExecutionsRequest listMonitoringExecutionsRequest)` Returns list of all monitoring job executions.
`ListMonitoringSchedulesResult`	`listMonitoringSchedules(ListMonitoringSchedulesRequest listMonitoringSchedulesRequest)` Returns list of all monitoring schedules.
`ListNotebookInstanceLifecycleConfigsResult`	`listNotebookInstanceLifecycleConfigs(ListNotebookInstanceLifecycleConfigsRequest listNotebookInstanceLifecycleConfigsRequest)` Lists notebook instance lifestyle configurations created with the CreateNotebookInstanceLifecycleConfig API.
`ListNotebookInstancesResult`	`listNotebookInstances(ListNotebookInstancesRequest listNotebookInstancesRequest)` Returns a list of the SageMaker notebook instances in the requester's account in an Amazon Web Services Region.
`ListOptimizationJobsResult`	`listOptimizationJobs(ListOptimizationJobsRequest listOptimizationJobsRequest)` Lists the optimization jobs in your account and their properties.
`ListPipelineExecutionsResult`	`listPipelineExecutions(ListPipelineExecutionsRequest listPipelineExecutionsRequest)` Gets a list of the pipeline executions.
`ListPipelineExecutionStepsResult`	`listPipelineExecutionSteps(ListPipelineExecutionStepsRequest listPipelineExecutionStepsRequest)` Gets a list of `PipeLineExecutionStep` objects.
`ListPipelineParametersForExecutionResult`	`listPipelineParametersForExecution(ListPipelineParametersForExecutionRequest listPipelineParametersForExecutionRequest)` Gets a list of parameters for a pipeline execution.
`ListPipelinesResult`	`listPipelines(ListPipelinesRequest listPipelinesRequest)` Gets a list of pipelines.
`ListProcessingJobsResult`	`listProcessingJobs(ListProcessingJobsRequest listProcessingJobsRequest)` Lists processing jobs that satisfy various filters.
`ListProjectsResult`	`listProjects(ListProjectsRequest listProjectsRequest)` Gets a list of the projects in an Amazon Web Services account.
`ListResourceCatalogsResult`	`listResourceCatalogs(ListResourceCatalogsRequest listResourceCatalogsRequest)` Lists Amazon SageMaker Catalogs based on given filters and orders.
`ListSpacesResult`	`listSpaces(ListSpacesRequest listSpacesRequest)` Lists spaces.
`ListStageDevicesResult`	`listStageDevices(ListStageDevicesRequest listStageDevicesRequest)` Lists devices allocated to the stage, containing detailed device information and deployment status.
`ListStudioLifecycleConfigsResult`	`listStudioLifecycleConfigs(ListStudioLifecycleConfigsRequest listStudioLifecycleConfigsRequest)` Lists the Amazon SageMaker Studio Lifecycle Configurations in your Amazon Web Services Account.
`ListSubscribedWorkteamsResult`	`listSubscribedWorkteams(ListSubscribedWorkteamsRequest listSubscribedWorkteamsRequest)` Gets a list of the work teams that you are subscribed to in the Amazon Web Services Marketplace.
`ListTagsResult`	`listTags(ListTagsRequest listTagsRequest)` Returns the tags for the specified SageMaker resource.
`ListTrainingJobsResult`	`listTrainingJobs(ListTrainingJobsRequest listTrainingJobsRequest)` Lists training jobs.
`ListTrainingJobsForHyperParameterTuningJobResult`	`listTrainingJobsForHyperParameterTuningJob(ListTrainingJobsForHyperParameterTuningJobRequest listTrainingJobsForHyperParameterTuningJobRequest)` Gets a list of TrainingJobSummary objects that describe the training jobs that a hyperparameter tuning job launched.
`ListTransformJobsResult`	`listTransformJobs(ListTransformJobsRequest listTransformJobsRequest)` Lists transform jobs.
`ListTrialComponentsResult`	`listTrialComponents(ListTrialComponentsRequest listTrialComponentsRequest)` Lists the trial components in your account.
`ListTrialsResult`	`listTrials(ListTrialsRequest listTrialsRequest)` Lists the trials in your account.
`ListUserProfilesResult`	`listUserProfiles(ListUserProfilesRequest listUserProfilesRequest)` Lists user profiles.
`ListWorkforcesResult`	`listWorkforces(ListWorkforcesRequest listWorkforcesRequest)` Use this operation to list all private and vendor workforces in an Amazon Web Services Region.
`ListWorkteamsResult`	`listWorkteams(ListWorkteamsRequest listWorkteamsRequest)` Gets a list of private work teams that you have defined in a region.
`PutModelPackageGroupPolicyResult`	`putModelPackageGroupPolicy(PutModelPackageGroupPolicyRequest putModelPackageGroupPolicyRequest)` Adds a resouce policy to control access to a model group.
`QueryLineageResult`	`queryLineage(QueryLineageRequest queryLineageRequest)` Use this action to inspect your lineage and discover relationships between entities.
`RegisterDevicesResult`	`registerDevices(RegisterDevicesRequest registerDevicesRequest)` Register devices.
`RenderUiTemplateResult`	`renderUiTemplate(RenderUiTemplateRequest renderUiTemplateRequest)` Renders the UI template so that you can preview the worker's experience.
`RetryPipelineExecutionResult`	`retryPipelineExecution(RetryPipelineExecutionRequest retryPipelineExecutionRequest)` Retry the execution of the pipeline.
`SearchResult`	`search(SearchRequest searchRequest)` Finds SageMaker resources that match a search query.
`SendPipelineExecutionStepFailureResult`	`sendPipelineExecutionStepFailure(SendPipelineExecutionStepFailureRequest sendPipelineExecutionStepFailureRequest)` Notifies the pipeline that the execution of a callback step failed, along with a message describing why.
`SendPipelineExecutionStepSuccessResult`	`sendPipelineExecutionStepSuccess(SendPipelineExecutionStepSuccessRequest sendPipelineExecutionStepSuccessRequest)` Notifies the pipeline that the execution of a callback step succeeded and provides a list of the step's output parameters.
`void`	`shutdown()` Shuts down this client object, releasing any resources that might be held open.
`StartEdgeDeploymentStageResult`	`startEdgeDeploymentStage(StartEdgeDeploymentStageRequest startEdgeDeploymentStageRequest)` Starts a stage in an edge deployment plan.
`StartInferenceExperimentResult`	`startInferenceExperiment(StartInferenceExperimentRequest startInferenceExperimentRequest)` Starts an inference experiment.
`StartMlflowTrackingServerResult`	`startMlflowTrackingServer(StartMlflowTrackingServerRequest startMlflowTrackingServerRequest)` Programmatically start an MLflow Tracking Server.
`StartMonitoringScheduleResult`	`startMonitoringSchedule(StartMonitoringScheduleRequest startMonitoringScheduleRequest)` Starts a previously stopped monitoring schedule.
`StartNotebookInstanceResult`	`startNotebookInstance(StartNotebookInstanceRequest startNotebookInstanceRequest)` Launches an ML compute instance with the latest version of the libraries and attaches your ML storage volume.
`StartPipelineExecutionResult`	`startPipelineExecution(StartPipelineExecutionRequest startPipelineExecutionRequest)` Starts a pipeline execution.
`StopAutoMLJobResult`	`stopAutoMLJob(StopAutoMLJobRequest stopAutoMLJobRequest)` A method for forcing a running job to shut down.
`StopCompilationJobResult`	`stopCompilationJob(StopCompilationJobRequest stopCompilationJobRequest)` Stops a model compilation job.
`StopEdgeDeploymentStageResult`	`stopEdgeDeploymentStage(StopEdgeDeploymentStageRequest stopEdgeDeploymentStageRequest)` Stops a stage in an edge deployment plan.
`StopEdgePackagingJobResult`	`stopEdgePackagingJob(StopEdgePackagingJobRequest stopEdgePackagingJobRequest)` Request to stop an edge packaging job.
`StopHyperParameterTuningJobResult`	`stopHyperParameterTuningJob(StopHyperParameterTuningJobRequest stopHyperParameterTuningJobRequest)` Stops a running hyperparameter tuning job and all running training jobs that the tuning job launched.
`StopInferenceExperimentResult`	`stopInferenceExperiment(StopInferenceExperimentRequest stopInferenceExperimentRequest)` Stops an inference experiment.
`StopInferenceRecommendationsJobResult`	`stopInferenceRecommendationsJob(StopInferenceRecommendationsJobRequest stopInferenceRecommendationsJobRequest)` Stops an Inference Recommender job.
`StopLabelingJobResult`	`stopLabelingJob(StopLabelingJobRequest stopLabelingJobRequest)` Stops a running labeling job.
`StopMlflowTrackingServerResult`	`stopMlflowTrackingServer(StopMlflowTrackingServerRequest stopMlflowTrackingServerRequest)` Programmatically stop an MLflow Tracking Server.
`StopMonitoringScheduleResult`	`stopMonitoringSchedule(StopMonitoringScheduleRequest stopMonitoringScheduleRequest)` Stops a previously started monitoring schedule.
`StopNotebookInstanceResult`	`stopNotebookInstance(StopNotebookInstanceRequest stopNotebookInstanceRequest)` Terminates the ML compute instance.
`StopOptimizationJobResult`	`stopOptimizationJob(StopOptimizationJobRequest stopOptimizationJobRequest)` Ends a running inference optimization job.
`StopPipelineExecutionResult`	`stopPipelineExecution(StopPipelineExecutionRequest stopPipelineExecutionRequest)` Stops a pipeline execution.
`StopProcessingJobResult`	`stopProcessingJob(StopProcessingJobRequest stopProcessingJobRequest)` Stops a processing job.
`StopTrainingJobResult`	`stopTrainingJob(StopTrainingJobRequest stopTrainingJobRequest)` Stops a training job.
`StopTransformJobResult`	`stopTransformJob(StopTransformJobRequest stopTransformJobRequest)` Stops a batch transform job.
`UpdateActionResult`	`updateAction(UpdateActionRequest updateActionRequest)` Updates an action.
`UpdateAppImageConfigResult`	`updateAppImageConfig(UpdateAppImageConfigRequest updateAppImageConfigRequest)` Updates the properties of an AppImageConfig.
`UpdateArtifactResult`	`updateArtifact(UpdateArtifactRequest updateArtifactRequest)` Updates an artifact.
`UpdateClusterResult`	`updateCluster(UpdateClusterRequest updateClusterRequest)` Updates a SageMaker HyperPod cluster.
`UpdateClusterSoftwareResult`	`updateClusterSoftware(UpdateClusterSoftwareRequest updateClusterSoftwareRequest)` Updates the platform software of a SageMaker HyperPod cluster for security patching.
`UpdateCodeRepositoryResult`	`updateCodeRepository(UpdateCodeRepositoryRequest updateCodeRepositoryRequest)` Updates the specified Git repository with the specified values.
`UpdateContextResult`	`updateContext(UpdateContextRequest updateContextRequest)` Updates a context.
`UpdateDeviceFleetResult`	`updateDeviceFleet(UpdateDeviceFleetRequest updateDeviceFleetRequest)` Updates a fleet of devices.
`UpdateDevicesResult`	`updateDevices(UpdateDevicesRequest updateDevicesRequest)` Updates one or more devices in a fleet.
`UpdateDomainResult`	`updateDomain(UpdateDomainRequest updateDomainRequest)` Updates the default settings for new user profiles in the domain.
`UpdateEndpointResult`	`updateEndpoint(UpdateEndpointRequest updateEndpointRequest)` Deploys the `EndpointConfig` specified in the request to a new fleet of instances.
`UpdateEndpointWeightsAndCapacitiesResult`	`updateEndpointWeightsAndCapacities(UpdateEndpointWeightsAndCapacitiesRequest updateEndpointWeightsAndCapacitiesRequest)` Updates variant weight of one or more variants associated with an existing endpoint, or capacity of one variant associated with an existing endpoint.
`UpdateExperimentResult`	`updateExperiment(UpdateExperimentRequest updateExperimentRequest)` Adds, updates, or removes the description of an experiment.
`UpdateFeatureGroupResult`	`updateFeatureGroup(UpdateFeatureGroupRequest updateFeatureGroupRequest)` Updates the feature group by either adding features or updating the online store configuration.
`UpdateFeatureMetadataResult`	`updateFeatureMetadata(UpdateFeatureMetadataRequest updateFeatureMetadataRequest)` Updates the description and parameters of the feature group.
`UpdateHubResult`	`updateHub(UpdateHubRequest updateHubRequest)` Update a hub.
`UpdateImageResult`	`updateImage(UpdateImageRequest updateImageRequest)` Updates the properties of a SageMaker image.
`UpdateImageVersionResult`	`updateImageVersion(UpdateImageVersionRequest updateImageVersionRequest)` Updates the properties of a SageMaker image version.
`UpdateInferenceComponentResult`	`updateInferenceComponent(UpdateInferenceComponentRequest updateInferenceComponentRequest)` Updates an inference component.
`UpdateInferenceComponentRuntimeConfigResult`	`updateInferenceComponentRuntimeConfig(UpdateInferenceComponentRuntimeConfigRequest updateInferenceComponentRuntimeConfigRequest)` Runtime settings for a model that is deployed with an inference component.
`UpdateInferenceExperimentResult`	`updateInferenceExperiment(UpdateInferenceExperimentRequest updateInferenceExperimentRequest)` Updates an inference experiment that you created.
`UpdateMlflowTrackingServerResult`	`updateMlflowTrackingServer(UpdateMlflowTrackingServerRequest updateMlflowTrackingServerRequest)` Updates properties of an existing MLflow Tracking Server.
`UpdateModelCardResult`	`updateModelCard(UpdateModelCardRequest updateModelCardRequest)` Update an Amazon SageMaker Model Card.
`UpdateModelPackageResult`	`updateModelPackage(UpdateModelPackageRequest updateModelPackageRequest)` Updates a versioned model.
`UpdateMonitoringAlertResult`	`updateMonitoringAlert(UpdateMonitoringAlertRequest updateMonitoringAlertRequest)` Update the parameters of a model monitor alert.
`UpdateMonitoringScheduleResult`	`updateMonitoringSchedule(UpdateMonitoringScheduleRequest updateMonitoringScheduleRequest)` Updates a previously created schedule.
`UpdateNotebookInstanceResult`	`updateNotebookInstance(UpdateNotebookInstanceRequest updateNotebookInstanceRequest)` Updates a notebook instance.
`UpdateNotebookInstanceLifecycleConfigResult`	`updateNotebookInstanceLifecycleConfig(UpdateNotebookInstanceLifecycleConfigRequest updateNotebookInstanceLifecycleConfigRequest)` Updates a notebook instance lifecycle configuration created with the CreateNotebookInstanceLifecycleConfig API.
`UpdatePipelineResult`	`updatePipeline(UpdatePipelineRequest updatePipelineRequest)` Updates a pipeline.
`UpdatePipelineExecutionResult`	`updatePipelineExecution(UpdatePipelineExecutionRequest updatePipelineExecutionRequest)` Updates a pipeline execution.
`UpdateProjectResult`	`updateProject(UpdateProjectRequest updateProjectRequest)` Updates a machine learning (ML) project that is created from a template that sets up an ML pipeline from training to deploying an approved model.
`UpdateSpaceResult`	`updateSpace(UpdateSpaceRequest updateSpaceRequest)` Updates the settings of a space.
`UpdateTrainingJobResult`	`updateTrainingJob(UpdateTrainingJobRequest updateTrainingJobRequest)` Update a model training job to request a new Debugger profiling configuration or to change warm pool retention length.
`UpdateTrialResult`	`updateTrial(UpdateTrialRequest updateTrialRequest)` Updates the display name of a trial.
`UpdateTrialComponentResult`	`updateTrialComponent(UpdateTrialComponentRequest updateTrialComponentRequest)` Updates one or more properties of a trial component.
`UpdateUserProfileResult`	`updateUserProfile(UpdateUserProfileRequest updateUserProfileRequest)` Updates a user profile.
`UpdateWorkforceResult`	`updateWorkforce(UpdateWorkforceRequest updateWorkforceRequest)` Use this operation to update your workforce.
`UpdateWorkteamResult`	`updateWorkteam(UpdateWorkteamRequest updateWorkteamRequest)` Updates an existing work team with new member definitions or description.
`AmazonSageMakerWaiters`	`waiters()`

- Field Detail
  - ENDPOINT_PREFIX
```
static final String ENDPOINT_PREFIX
```
    The region metadata service name for computing region endpoints. You can use this value to retrieve metadata (such as supported regions) of the service.
    
    See Also:
    
    RegionUtils.getRegionsForService(String), Constant Field Values
- Method Detail
  - addAssociation
```
AddAssociationResult addAssociation(AddAssociationRequest addAssociationRequest)
```
    Creates an association between the source and the destination. A source can be associated with multiple destinations, and a destination can be associated with multiple sources. An association is a lineage tracking entity. For more information, see Amazon SageMaker ML Lineage Tracking.
    
    Parameters:
    
    addAssociationRequest -
    
    Returns:
    
    Result of the AddAssociation operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - addTags
```
AddTagsResult addTags(AddTagsRequest addTagsRequest)
```
    Adds or overwrites one or more tags for the specified SageMaker resource. You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints.
    
    Each tag consists of a key and an optional value. Tag keys must be unique per resource. For more information about tags, see For more information, see Amazon Web Services Tagging Strategies.
    
    Tags that you add to a hyperparameter tuning job by calling this API are also added to any training jobs that the hyperparameter tuning job launches after you call this API, but not to training jobs that the hyperparameter tuning job launched before you called this API. To make sure that the tags associated with a hyperparameter tuning job are also added to all training jobs that the hyperparameter tuning job launches, add the tags when you first create the tuning job by specifying them in the Tags parameter of CreateHyperParameterTuningJob
    
    Tags that you add to a SageMaker Domain or User Profile by calling this API are also added to any Apps that the Domain or User Profile launches after you call this API, but not to Apps that the Domain or User Profile launched before you called this API. To make sure that the tags associated with a Domain or User Profile are also added to all Apps that the Domain or User Profile launches, add the tags when you first create the Domain or User Profile by specifying them in the Tags parameter of CreateDomain or CreateUserProfile.
    
    Parameters:
    
    addTagsRequest -
    
    Returns:
    
    Result of the AddTags operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - associateTrialComponent
```
AssociateTrialComponentResult associateTrialComponent(AssociateTrialComponentRequest associateTrialComponentRequest)
```
    Associates a trial component with a trial. A trial component can be associated with multiple trials. To disassociate a trial component from a trial, call the DisassociateTrialComponent API.
    
    Parameters:
    
    associateTrialComponentRequest -
    
    Returns:
    
    Result of the AssociateTrialComponent operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - batchDescribeModelPackage
```
BatchDescribeModelPackageResult batchDescribeModelPackage(BatchDescribeModelPackageRequest batchDescribeModelPackageRequest)
```
    This action batch describes a list of versioned model packages
    
    Parameters:
    
    batchDescribeModelPackageRequest -
    
    Returns:
    
    Result of the BatchDescribeModelPackage operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - createAction
```
CreateActionResult createAction(CreateActionRequest createActionRequest)
```
    Creates an action. An action is a lineage tracking entity that represents an action or activity. For example, a model deployment or an HPO job. Generally, an action involves at least one input or output artifact. For more information, see Amazon SageMaker ML Lineage Tracking.
    
    Parameters:
    
    createActionRequest -
    
    Returns:
    
    Result of the CreateAction operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createAlgorithm
```
CreateAlgorithmResult createAlgorithm(CreateAlgorithmRequest createAlgorithmRequest)
```
    Create a machine learning algorithm that you can use in SageMaker and list in the Amazon Web Services Marketplace.
    
    Parameters:
    
    createAlgorithmRequest -
    
    Returns:
    
    Result of the CreateAlgorithm operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - createApp
```
CreateAppResult createApp(CreateAppRequest createAppRequest)
```
    Creates a running app for the specified UserProfile. This operation is automatically invoked by Amazon SageMaker upon access to the associated Domain, and when new kernel configurations are selected by the user. A user may have multiple Apps active simultaneously.
    
    Parameters:
    
    createAppRequest -
    
    Returns:
    
    Result of the CreateApp operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    ResourceInUseException - Resource being accessed is in use.
    
    See Also:
    
    AWS API Documentation
  - createAppImageConfig
```
CreateAppImageConfigResult createAppImageConfig(CreateAppImageConfigRequest createAppImageConfigRequest)
```
    Creates a configuration for running a SageMaker image as a KernelGateway app. The configuration specifies the Amazon Elastic File System storage volume on the image, and a list of the kernels in the image.
    
    Parameters:
    
    createAppImageConfigRequest -
    
    Returns:
    
    Result of the CreateAppImageConfig operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    See Also:
    
    AWS API Documentation
  - createArtifact
```
CreateArtifactResult createArtifact(CreateArtifactRequest createArtifactRequest)
```
    Creates an artifact. An artifact is a lineage tracking entity that represents a URI addressable object or data. Some examples are the S3 URI of a dataset and the ECR registry path of an image. For more information, see Amazon SageMaker ML Lineage Tracking.
    
    Parameters:
    
    createArtifactRequest -
    
    Returns:
    
    Result of the CreateArtifact operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createAutoMLJob
```
CreateAutoMLJobResult createAutoMLJob(CreateAutoMLJobRequest createAutoMLJobRequest)
```
    Creates an Autopilot job also referred to as Autopilot experiment or AutoML job.
    
    We recommend using the new versions CreateAutoMLJobV2 and DescribeAutoMLJobV2, which offer backward compatibility.
    
    CreateAutoMLJobV2 can manage tabular problem types identical to those of its previous version CreateAutoMLJob, as well as time-series forecasting, non-tabular problem types such as image or text classification, and text generation (LLMs fine-tuning).
    
    Find guidelines about how to migrate a CreateAutoMLJob to CreateAutoMLJobV2 in Migrate a CreateAutoMLJob to CreateAutoMLJobV2.
    
    You can find the best-performing model after you run an AutoML job by calling DescribeAutoMLJobV2 (recommended) or DescribeAutoMLJob.
    
    Parameters:
    
    createAutoMLJobRequest -
    
    Returns:
    
    Result of the CreateAutoMLJob operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createAutoMLJobV2
```
CreateAutoMLJobV2Result createAutoMLJobV2(CreateAutoMLJobV2Request createAutoMLJobV2Request)
```
    Creates an Autopilot job also referred to as Autopilot experiment or AutoML job V2.
    
    CreateAutoMLJobV2 and DescribeAutoMLJobV2 are new versions of CreateAutoMLJob and DescribeAutoMLJob which offer backward compatibility.
    
    CreateAutoMLJobV2 can manage tabular problem types identical to those of its previous version CreateAutoMLJob, as well as time-series forecasting, non-tabular problem types such as image or text classification, and text generation (LLMs fine-tuning).
    
    Find guidelines about how to migrate a CreateAutoMLJob to CreateAutoMLJobV2 in Migrate a CreateAutoMLJob to CreateAutoMLJobV2.
    
    For the list of available problem types supported by CreateAutoMLJobV2, see AutoMLProblemTypeConfig.
    
    You can find the best-performing model after you run an AutoML job V2 by calling DescribeAutoMLJobV2.
    
    Parameters:
    
    createAutoMLJobV2Request -
    
    Returns:
    
    Result of the CreateAutoMLJobV2 operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createCluster
```
CreateClusterResult createCluster(CreateClusterRequest createClusterRequest)
```
    Creates a SageMaker HyperPod cluster. SageMaker HyperPod is a capability of SageMaker for creating and managing persistent clusters for developing large machine learning models, such as large language models (LLMs) and diffusion models. To learn more, see Amazon SageMaker HyperPod in the Amazon SageMaker Developer Guide.
    
    Parameters:
    
    createClusterRequest -
    
    Returns:
    
    Result of the CreateCluster operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    ResourceInUseException - Resource being accessed is in use.
    
    See Also:
    
    AWS API Documentation
  - createCodeRepository
```
CreateCodeRepositoryResult createCodeRepository(CreateCodeRepositoryRequest createCodeRepositoryRequest)
```
    Creates a Git repository as a resource in your SageMaker account. You can associate the repository with notebook instances so that you can use Git source control for the notebooks you create. The Git repository is a resource in your SageMaker account, so it can be associated with more than one notebook instance, and it persists independently from the lifecycle of any notebook instances it is associated with.
    
    The repository can be hosted either in Amazon Web Services CodeCommit or in any other Git repository.
    
    Parameters:
    
    createCodeRepositoryRequest -
    
    Returns:
    
    Result of the CreateCodeRepository operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - createCompilationJob
```
CreateCompilationJobResult createCompilationJob(CreateCompilationJobRequest createCompilationJobRequest)
```
    Starts a model compilation job. After the model has been compiled, Amazon SageMaker saves the resulting model artifacts to an Amazon Simple Storage Service (Amazon S3) bucket that you specify.
    
    If you choose to host your model using Amazon SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts with Amazon Web Services IoT Greengrass. In that case, deploy them as an ML resource.
    
    In the request body, you provide the following:
    - A name for the compilation job
    - Information about the input model artifacts
    - The output location for the compiled model and the device (target) that the model runs on
    - The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker assumes to perform the model compilation job.
    You can also provide a Tag to track the model compilation job's resource use and costs. The response body contains the CompilationJobArn for the compiled job.
    
    To stop a model compilation job, use StopCompilationJob. To get information about a particular model compilation job, use DescribeCompilationJob. To get information about multiple model compilation jobs, use ListCompilationJobs.
    Parameters:
    
    createCompilationJobRequest -
    
    Returns:
    
    Result of the CreateCompilationJob operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createContext
```
CreateContextResult createContext(CreateContextRequest createContextRequest)
```
    Creates a context. A context is a lineage tracking entity that represents a logical grouping of other tracking or experiment entities. Some examples are an endpoint and a model package. For more information, see Amazon SageMaker ML Lineage Tracking.
    
    Parameters:
    
    createContextRequest -
    
    Returns:
    
    Result of the CreateContext operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createDataQualityJobDefinition
```
CreateDataQualityJobDefinitionResult createDataQualityJobDefinition(CreateDataQualityJobDefinitionRequest createDataQualityJobDefinitionRequest)
```
    Creates a definition for a job that monitors data quality and drift. For information about model monitor, see Amazon SageMaker Model Monitor.
    
    Parameters:
    
    createDataQualityJobDefinitionRequest -
    
    Returns:
    
    Result of the CreateDataQualityJobDefinition operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    ResourceInUseException - Resource being accessed is in use.
    
    See Also:
    
    AWS API Documentation
  - createDeviceFleet
```
CreateDeviceFleetResult createDeviceFleet(CreateDeviceFleetRequest createDeviceFleetRequest)
```
    Creates a device fleet.
    
    Parameters:
    
    createDeviceFleetRequest -
    
    Returns:
    
    Result of the CreateDeviceFleet operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createDomain
```
CreateDomainResult createDomain(CreateDomainRequest createDomainRequest)
```
    Creates a Domain. A domain consists of an associated Amazon Elastic File System volume, a list of authorized users, and a variety of security, application, policy, and Amazon Virtual Private Cloud (VPC) configurations. Users within a domain can share notebook files and other artifacts with each other.
    
    EFS storage
    
    When a domain is created, an EFS volume is created for use by all of the users within the domain. Each user receives a private home directory within the EFS volume for notebooks, Git repositories, and data files.
    
    SageMaker uses the Amazon Web Services Key Management Service (Amazon Web Services KMS) to encrypt the EFS volume attached to the domain with an Amazon Web Services managed key by default. For more control, you can specify a customer managed key. For more information, see Protect Data at Rest Using Encryption.
    
    VPC configuration
    
    All traffic between the domain and the Amazon EFS volume is through the specified VPC and subnets. For other traffic, you can specify the AppNetworkAccessType parameter. AppNetworkAccessType corresponds to the network access type that you choose when you onboard to the domain. The following options are available:
    - PublicInternetOnly - Non-EFS traffic goes through a VPC managed by Amazon SageMaker, which allows internet access. This is the default value.
    - VpcOnly - All traffic is through the specified VPC and subnets. Internet access is disabled by default. To allow internet access, you must specify a NAT gateway.
      
      When internet access is disabled, you won't be able to run a Amazon SageMaker Studio notebook or to train or host models unless your VPC has an interface endpoint to the SageMaker API and runtime or a NAT gateway and your security groups allow outbound connections.
    NFS traffic over TCP on port 2049 needs to be allowed in both inbound and outbound rules in order to launch a Amazon SageMaker Studio app successfully.
    
    For more information, see Connect Amazon SageMaker Studio Notebooks to Resources in a VPC.
    Parameters:
    
    createDomainRequest -
    
    Returns:
    
    Result of the CreateDomain operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    ResourceInUseException - Resource being accessed is in use.
    
    See Also:
    
    AWS API Documentation
  - createEdgeDeploymentPlan
```
CreateEdgeDeploymentPlanResult createEdgeDeploymentPlan(CreateEdgeDeploymentPlanRequest createEdgeDeploymentPlanRequest)
```
    Creates an edge deployment plan, consisting of multiple stages. Each stage may have a different deployment configuration and devices.
    
    Parameters:
    
    createEdgeDeploymentPlanRequest -
    
    Returns:
    
    Result of the CreateEdgeDeploymentPlan operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createEdgeDeploymentStage
```
CreateEdgeDeploymentStageResult createEdgeDeploymentStage(CreateEdgeDeploymentStageRequest createEdgeDeploymentStageRequest)
```
    Creates a new stage in an existing edge deployment plan.
    
    Parameters:
    
    createEdgeDeploymentStageRequest -
    
    Returns:
    
    Result of the CreateEdgeDeploymentStage operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createEdgePackagingJob
```
CreateEdgePackagingJobResult createEdgePackagingJob(CreateEdgePackagingJobRequest createEdgePackagingJobRequest)
```
    Starts a SageMaker Edge Manager model packaging job. Edge Manager will use the model artifacts from the Amazon Simple Storage Service bucket that you specify. After the model has been packaged, Amazon SageMaker saves the resulting artifacts to an S3 bucket that you specify.
    
    Parameters:
    
    createEdgePackagingJobRequest -
    
    Returns:
    
    Result of the CreateEdgePackagingJob operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createEndpoint
```
CreateEndpointResult createEndpoint(CreateEndpointRequest createEndpointRequest)
```
    Creates an endpoint using the endpoint configuration specified in the request. SageMaker uses the endpoint to provision resources and deploy models. You create the endpoint configuration with the CreateEndpointConfig API.
    
    Use this API to deploy models using SageMaker hosting services.
    
    You must not delete an EndpointConfig that is in use by an endpoint that is live or while the UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. To update an endpoint, you must create a new EndpointConfig.
    
    The endpoint name must be unique within an Amazon Web Services Region in your Amazon Web Services account.
    
    When it receives the request, SageMaker creates the endpoint, launches the resources (ML compute instances), and deploys the model(s) on them.
    
    When you call CreateEndpoint, a load call is made to DynamoDB to verify that your endpoint configuration exists. When you read data from a DynamoDB table supporting Eventually Consistent Reads , the response might not reflect the results of a recently completed write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB, this causes a validation error. If you repeat your read request after a short time, the response should return the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a DynamoDB eventually consistent read.
    
    When SageMaker receives the request, it sets the endpoint status to Creating. After it creates the endpoint, it sets the status to InService. SageMaker can then process incoming requests for inferences. To check the status of an endpoint, use the DescribeEndpoint API.
    
    If any of the models hosted at this endpoint get model data from an Amazon S3 location, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provided. Amazon Web Services STS is activated in your Amazon Web Services account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide.
    To add the IAM role policies for using this API operation, go to the IAM console, and choose Roles in the left navigation pane. Search the IAM role that you want to grant access to use the CreateEndpoint and CreateEndpointConfig API operations, add the following policies to the role.
    
    Option 1: For a full SageMaker access, search and attach the AmazonSageMakerFullAccess policy.
    
    Option 2: For granting a limited access to an IAM role, paste the following Action elements manually into the JSON file of the IAM role:
    
    "Action": ["sagemaker:CreateEndpoint", "sagemaker:CreateEndpointConfig"]
    
    "Resource": [
    
    "arn:aws:sagemaker:region:account-id:endpoint/endpointName"
    
    "arn:aws:sagemaker:region:account-id:endpoint-config/endpointConfigName"
    
    ]
    
    For more information, see SageMaker API Permissions: Actions, Permissions, and Resources Reference.
    Parameters:
    
    createEndpointRequest -
    
    Returns:
    
    Result of the CreateEndpoint operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createEndpointConfig
```
CreateEndpointConfigResult createEndpointConfig(CreateEndpointConfigRequest createEndpointConfigRequest)
```
    Creates an endpoint configuration that SageMaker hosting services uses to deploy models. In the configuration, you identify one or more models, created using the CreateModel API, to deploy and the resources that you want SageMaker to provision. Then you call the CreateEndpoint API.
    
    Use this API if you want to use SageMaker hosting services to deploy models into production.
    
    In the request, you define a ProductionVariant, for each model that you want to deploy. Each ProductionVariant parameter also describes the resources that you want SageMaker to provision. This includes the number and type of ML compute instances to deploy.
    
    If you are hosting multiple models, you also assign a VariantWeight to specify how much traffic you want to allocate to each model. For example, suppose that you want to host two models, A and B, and you assign traffic weight 2 for model A and 1 for model B. SageMaker distributes two-thirds of the traffic to Model A, and one-third to model B.
    
    When you call CreateEndpoint, a load call is made to DynamoDB to verify that your endpoint configuration exists. When you read data from a DynamoDB table supporting Eventually Consistent Reads , the response might not reflect the results of a recently completed write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB, this causes a validation error. If you repeat your read request after a short time, the response should return the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a DynamoDB eventually consistent read.
    
    Parameters:
    
    createEndpointConfigRequest -
    
    Returns:
    
    Result of the CreateEndpointConfig operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createExperiment
```
CreateExperimentResult createExperiment(CreateExperimentRequest createExperimentRequest)
```
    Creates a SageMaker experiment. An experiment is a collection of trials that are observed, compared and evaluated as a group. A trial is a set of steps, called trial components, that produce a machine learning model.
    
    In the Studio UI, trials are referred to as run groups and trial components are referred to as runs.
    
    The goal of an experiment is to determine the components that produce the best model. Multiple trials are performed, each one isolating and measuring the impact of a change to one or more inputs, while keeping the remaining inputs constant.
    
    When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK.
    
    You can add tags to experiments, trials, trial components and then use the Search API to search for the tags.
    
    To add a description to an experiment, specify the optional Description parameter. To add a description later, or to change the description, call the UpdateExperiment API.
    
    To get a list of all your experiments, call the ListExperiments API. To view an experiment's properties, call the DescribeExperiment API. To get a list of all the trials associated with an experiment, call the ListTrials API. To create a trial call the CreateTrial API.
    
    Parameters:
    
    createExperimentRequest -
    
    Returns:
    
    Result of the CreateExperiment operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createFeatureGroup
```
CreateFeatureGroupResult createFeatureGroup(CreateFeatureGroupRequest createFeatureGroupRequest)
```
    Create a new FeatureGroup. A FeatureGroup is a group of Features defined in the FeatureStore to describe a Record.
    
    The FeatureGroup defines the schema and features contained in the FeatureGroup. A FeatureGroup definition is composed of a list of Features, a RecordIdentifierFeatureName, an EventTimeFeatureName and configurations for its OnlineStore and OfflineStore. Check Amazon Web Services service quotas to see the FeatureGroups quota for your Amazon Web Services account.
    
    Note that it can take approximately 10-15 minutes to provision an OnlineStore FeatureGroup with the InMemory StorageType.
    
    You must include at least one of OnlineStoreConfig and OfflineStoreConfig to create a FeatureGroup.
    
    Parameters:
    
    createFeatureGroupRequest -
    
    Returns:
    
    Result of the CreateFeatureGroup operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createFlowDefinition
```
CreateFlowDefinitionResult createFlowDefinition(CreateFlowDefinitionRequest createFlowDefinitionRequest)
```
    Creates a flow definition.
    
    Parameters:
    
    createFlowDefinitionRequest -
    
    Returns:
    
    Result of the CreateFlowDefinition operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    ResourceInUseException - Resource being accessed is in use.
    
    See Also:
    
    AWS API Documentation
  - createHub
```
CreateHubResult createHub(CreateHubRequest createHubRequest)
```
    Create a hub.
    
    Parameters:
    
    createHubRequest -
    
    Returns:
    
    Result of the CreateHub operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createHubContentReference
```
CreateHubContentReferenceResult createHubContentReference(CreateHubContentReferenceRequest createHubContentReferenceRequest)
```
    Create a hub content reference in order to add a model in the JumpStart public hub to a private hub.
    
    Parameters:
    
    createHubContentReferenceRequest -
    
    Returns:
    
    Result of the CreateHubContentReference operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createHumanTaskUi
```
CreateHumanTaskUiResult createHumanTaskUi(CreateHumanTaskUiRequest createHumanTaskUiRequest)
```
    Defines the settings you will use for the human review workflow user interface. Reviewers will see a three-panel interface with an instruction area, the item to review, and an input area.
    
    Parameters:
    
    createHumanTaskUiRequest -
    
    Returns:
    
    Result of the CreateHumanTaskUi operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    ResourceInUseException - Resource being accessed is in use.
    
    See Also:
    
    AWS API Documentation
  - createHyperParameterTuningJob
```
CreateHyperParameterTuningJobResult createHyperParameterTuningJob(CreateHyperParameterTuningJobRequest createHyperParameterTuningJobRequest)
```
    Starts a hyperparameter tuning job. A hyperparameter tuning job finds the best version of a model by running many training jobs on your dataset using the algorithm you choose and values for hyperparameters within ranges that you specify. It then chooses the hyperparameter values that result in a model that performs the best, as measured by an objective metric that you choose.
    
    A hyperparameter tuning job automatically creates Amazon SageMaker experiments, trials, and trial components for each training job that it runs. You can view these entities in Amazon SageMaker Studio. For more information, see View Experiments, Trials, and Trial Components.
    
    Do not include any security-sensitive information including account access IDs, secrets or tokens in any hyperparameter field. If the use of security-sensitive credentials are detected, SageMaker will reject your training job request and return an exception error.
    
    Parameters:
    
    createHyperParameterTuningJobRequest -
    
    Returns:
    
    Result of the CreateHyperParameterTuningJob operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createImage
```
CreateImageResult createImage(CreateImageRequest createImageRequest)
```
    Creates a custom SageMaker image. A SageMaker image is a set of image versions. Each image version represents a container image stored in Amazon ECR. For more information, see Bring your own SageMaker image.
    
    Parameters:
    
    createImageRequest -
    
    Returns:
    
    Result of the CreateImage operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createImageVersion
```
CreateImageVersionResult createImageVersion(CreateImageVersionRequest createImageVersionRequest)
```
    Creates a version of the SageMaker image specified by ImageName. The version represents the Amazon ECR container image specified by BaseImage.
    
    Parameters:
    
    createImageVersionRequest -
    
    Returns:
    
    Result of the CreateImageVersion operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - createInferenceComponent
```
CreateInferenceComponentResult createInferenceComponent(CreateInferenceComponentRequest createInferenceComponentRequest)
```
    Creates an inference component, which is a SageMaker hosting object that you can use to deploy a model to an endpoint. In the inference component settings, you specify the model, the endpoint, and how the model utilizes the resources that the endpoint hosts. You can optimize resource utilization by tailoring how the required CPU cores, accelerators, and memory are allocated. You can deploy multiple inference components to an endpoint, where each inference component contains one model and the resource utilization needs for that individual model. After you deploy an inference component, you can directly invoke the associated model when you use the InvokeEndpoint API action.
    
    Parameters:
    
    createInferenceComponentRequest -
    
    Returns:
    
    Result of the CreateInferenceComponent operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createInferenceExperiment
```
CreateInferenceExperimentResult createInferenceExperiment(CreateInferenceExperimentRequest createInferenceExperimentRequest)
```
    Creates an inference experiment using the configurations specified in the request.
    
    Use this API to setup and schedule an experiment to compare model variants on a Amazon SageMaker inference endpoint. For more information about inference experiments, see Shadow tests.
    
    Amazon SageMaker begins your experiment at the scheduled time and routes traffic to your endpoint's model variants based on your specified configuration.
    
    While the experiment is in progress or after it has concluded, you can view metrics that compare your model variants. For more information, see View, monitor, and edit shadow tests.
    
    Parameters:
    
    createInferenceExperimentRequest -
    
    Returns:
    
    Result of the CreateInferenceExperiment operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createInferenceRecommendationsJob
```
CreateInferenceRecommendationsJobResult createInferenceRecommendationsJob(CreateInferenceRecommendationsJobRequest createInferenceRecommendationsJobRequest)
```
    Starts a recommendation job. You can create either an instance recommendation or load test job.
    
    Parameters:
    
    createInferenceRecommendationsJobRequest -
    
    Returns:
    
    Result of the CreateInferenceRecommendationsJob operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createLabelingJob
```
CreateLabelingJobResult createLabelingJob(CreateLabelingJobRequest createLabelingJobRequest)
```
    Creates a job that uses workers to label the data objects in your input dataset. You can use the labeled data to train machine learning models.
    
    You can select your workforce from one of three providers:
    - A private workforce that you create. It can include employees, contractors, and outside experts. Use a private workforce when want the data to stay within your organization or when a specific set of skills is required.
    - One or more vendors that you select from the Amazon Web Services Marketplace. Vendors provide expertise in specific areas.
    - The Amazon Mechanical Turk workforce. This is the largest workforce, but it should only be used for public data or data that has been stripped of any personally identifiable information.
    You can also use automated data labeling to reduce the number of data objects that need to be labeled by a human. Automated data labeling uses active learning to determine if a data object can be labeled by machine or if it needs to be sent to a human worker. For more information, see Using Automated Data Labeling.
    
    The data objects to be labeled are contained in an Amazon S3 bucket. You create a manifest file that describes the location of each object. For more information, see Using Input and Output Data.
    
    The output can be used as the manifest file for another labeling job or as training data for your machine learning models.
    
    You can use this operation to create a static labeling job or a streaming labeling job. A static labeling job stops if all data objects in the input manifest file identified in ManifestS3Uri have been labeled. A streaming labeling job runs perpetually until it is manually stopped, or remains idle for 10 days. You can send new data objects to an active (InProgress) streaming labeling job in real time. To learn how to create a static labeling job, see Create a Labeling Job (API) in the Amazon SageMaker Developer Guide. To learn how to create a streaming labeling job, see Create a Streaming Labeling Job.
    Parameters:
    
    createLabelingJobRequest -
    
    Returns:
    
    Result of the CreateLabelingJob operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createMlflowTrackingServer
```
CreateMlflowTrackingServerResult createMlflowTrackingServer(CreateMlflowTrackingServerRequest createMlflowTrackingServerRequest)
```
    Creates an MLflow Tracking Server using a general purpose Amazon S3 bucket as the artifact store. For more information, see Create an MLflow Tracking Server.
    
    Parameters:
    
    createMlflowTrackingServerRequest -
    
    Returns:
    
    Result of the CreateMlflowTrackingServer operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createModel
```
CreateModelResult createModel(CreateModelRequest createModelRequest)
```
    Creates a model in SageMaker. In the request, you name the model and describe a primary container. For the primary container, you specify the Docker image that contains inference code, artifacts (from prior training), and a custom environment map that the inference code uses when you deploy the model for predictions.
    
    Use this API to create a model if you want to use SageMaker hosting services or run a batch transform job.
    
    To host your model, you create an endpoint configuration with the CreateEndpointConfig API, and then create an endpoint with the CreateEndpoint API. SageMaker then deploys all of the containers that you defined for the model in the hosting environment.
    
    To run a batch transform using your model, you start a job with the CreateTransformJob API. SageMaker uses your model and your dataset to get inferences which are then saved to a specified S3 location.
    
    In the request, you also provide an IAM role that SageMaker can assume to access model artifacts and docker image for deployment on ML compute hosting instances or for batch transform jobs. In addition, you also use the IAM role to manage permissions the inference code needs. For example, if the inference code access any other Amazon Web Services resources, you grant necessary permissions via this role.
    
    Parameters:
    
    createModelRequest -
    
    Returns:
    
    Result of the CreateModel operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createModelBiasJobDefinition
```
CreateModelBiasJobDefinitionResult createModelBiasJobDefinition(CreateModelBiasJobDefinitionRequest createModelBiasJobDefinitionRequest)
```
    Creates the definition for a model bias job.
    
    Parameters:
    
    createModelBiasJobDefinitionRequest -
    
    Returns:
    
    Result of the CreateModelBiasJobDefinition operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    ResourceInUseException - Resource being accessed is in use.
    
    See Also:
    
    AWS API Documentation
  - createModelCard
```
CreateModelCardResult createModelCard(CreateModelCardRequest createModelCardRequest)
```
    Creates an Amazon SageMaker Model Card.
    
    For information about how to use model cards, see Amazon SageMaker Model Card.
    
    Parameters:
    
    createModelCardRequest -
    
    Returns:
    
    Result of the CreateModelCard operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    ConflictException - There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.
    
    See Also:
    
    AWS API Documentation
  - createModelCardExportJob
```
CreateModelCardExportJobResult createModelCardExportJob(CreateModelCardExportJobRequest createModelCardExportJobRequest)
```
    Creates an Amazon SageMaker Model Card export job.
    
    Parameters:
    
    createModelCardExportJobRequest -
    
    Returns:
    
    Result of the CreateModelCardExportJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    ConflictException - There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.
    
    See Also:
    
    AWS API Documentation
  - createModelExplainabilityJobDefinition
```
CreateModelExplainabilityJobDefinitionResult createModelExplainabilityJobDefinition(CreateModelExplainabilityJobDefinitionRequest createModelExplainabilityJobDefinitionRequest)
```
    Creates the definition for a model explainability job.
    
    Parameters:
    
    createModelExplainabilityJobDefinitionRequest -
    
    Returns:
    
    Result of the CreateModelExplainabilityJobDefinition operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    ResourceInUseException - Resource being accessed is in use.
    
    See Also:
    
    AWS API Documentation
  - createModelPackage
```
CreateModelPackageResult createModelPackage(CreateModelPackageRequest createModelPackageRequest)
```
    Creates a model package that you can use to create SageMaker models or list on Amazon Web Services Marketplace, or a versioned model that is part of a model group. Buyers can subscribe to model packages listed on Amazon Web Services Marketplace to create models in SageMaker.
    
    To create a model package by specifying a Docker container that contains your inference code and the Amazon S3 location of your model artifacts, provide values for InferenceSpecification. To create a model from an algorithm resource that you created or subscribed to in Amazon Web Services Marketplace, provide a value for SourceAlgorithmSpecification.
    There are two types of model packages:
    
    Versioned - a model that is part of a model group in the model registry.
    
    Unversioned - a model package that is not part of a model group.
    Parameters:
    
    createModelPackageRequest -
    
    Returns:
    
    Result of the CreateModelPackage operation returned by the service.
    
    Throws:
    
    ConflictException - There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createModelPackageGroup
```
CreateModelPackageGroupResult createModelPackageGroup(CreateModelPackageGroupRequest createModelPackageGroupRequest)
```
    Creates a model group. A model group contains a group of model versions.
    
    Parameters:
    
    createModelPackageGroupRequest -
    
    Returns:
    
    Result of the CreateModelPackageGroup operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createModelQualityJobDefinition
```
CreateModelQualityJobDefinitionResult createModelQualityJobDefinition(CreateModelQualityJobDefinitionRequest createModelQualityJobDefinitionRequest)
```
    Creates a definition for a job that monitors model quality and drift. For information about model monitor, see Amazon SageMaker Model Monitor.
    
    Parameters:
    
    createModelQualityJobDefinitionRequest -
    
    Returns:
    
    Result of the CreateModelQualityJobDefinition operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    ResourceInUseException - Resource being accessed is in use.
    
    See Also:
    
    AWS API Documentation
  - createMonitoringSchedule
```
CreateMonitoringScheduleResult createMonitoringSchedule(CreateMonitoringScheduleRequest createMonitoringScheduleRequest)
```
    Creates a schedule that regularly starts Amazon SageMaker Processing Jobs to monitor the data captured for an Amazon SageMaker Endpoint.
    
    Parameters:
    
    createMonitoringScheduleRequest -
    
    Returns:
    
    Result of the CreateMonitoringSchedule operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    ResourceInUseException - Resource being accessed is in use.
    
    See Also:
    
    AWS API Documentation
  - createNotebookInstance
```
CreateNotebookInstanceResult createNotebookInstance(CreateNotebookInstanceRequest createNotebookInstanceRequest)
```
    Creates an SageMaker notebook instance. A notebook instance is a machine learning (ML) compute instance running on a Jupyter notebook.
    
    In a CreateNotebookInstance request, specify the type of ML compute instance that you want to run. SageMaker launches the instance, installs common libraries that you can use to explore datasets for model training, and attaches an ML storage volume to the notebook instance.
    
    SageMaker also provides a set of example notebooks. Each notebook demonstrates how to use SageMaker with a specific algorithm or with a machine learning framework.
    
    After receiving the request, SageMaker does the following:
    1. Creates a network interface in the SageMaker VPC.
    2. (Option) If you specified SubnetId, SageMaker creates a network interface in your own VPC, which is inferred from the subnet ID that you provide in the input. When creating this network interface, SageMaker attaches the security group that you specified in the request to the network interface that it creates in your VPC.
    3. Launches an EC2 instance of the type specified in the request in the SageMaker VPC. If you specified SubnetId of your VPC, SageMaker specifies both network interfaces when launching this instance. This enables inbound traffic from your own VPC to the notebook instance, assuming that the security groups allow it.
    After creating the notebook instance, SageMaker returns its Amazon Resource Name (ARN). You can't change the name of a notebook instance after you create it.
    
    After SageMaker creates the notebook instance, you can connect to the Jupyter server and work in Jupyter notebooks. For example, you can write code to explore a dataset that you can use for model training, train a model, host models by creating SageMaker endpoints, and validate hosted models.
    
    For more information, see How It Works.
    Parameters:
    
    createNotebookInstanceRequest -
    
    Returns:
    
    Result of the CreateNotebookInstance operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createNotebookInstanceLifecycleConfig
```
CreateNotebookInstanceLifecycleConfigResult createNotebookInstanceLifecycleConfig(CreateNotebookInstanceLifecycleConfigRequest createNotebookInstanceLifecycleConfigRequest)
```
    Creates a lifecycle configuration that you can associate with a notebook instance. A lifecycle configuration is a collection of shell scripts that run when you create or start a notebook instance.
    
    Each lifecycle configuration script has a limit of 16384 characters.
    
    The value of the $PATH environment variable that is available to both scripts is /sbin:bin:/usr/sbin:/usr/bin.
    
    View Amazon CloudWatch Logs for notebook instance lifecycle configurations in log group /aws/sagemaker/NotebookInstances in log stream [notebook-instance-name]/[LifecycleConfigHook].
    
    Lifecycle configuration scripts cannot run for longer than 5 minutes. If a script runs for longer than 5 minutes, it fails and the notebook instance is not created or started.
    
    For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.
    
    Parameters:
    
    createNotebookInstanceLifecycleConfigRequest -
    
    Returns:
    
    Result of the CreateNotebookInstanceLifecycleConfig operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createOptimizationJob
```
CreateOptimizationJobResult createOptimizationJob(CreateOptimizationJobRequest createOptimizationJobRequest)
```
    Creates a job that optimizes a model for inference performance. To create the job, you provide the location of a source model, and you provide the settings for the optimization techniques that you want the job to apply. When the job completes successfully, SageMaker uploads the new optimized model to the output destination that you specify.
    
    For more information about how to use this action, and about the supported optimization techniques, see Optimize model inference with Amazon SageMaker.
    
    Parameters:
    
    createOptimizationJobRequest -
    
    Returns:
    
    Result of the CreateOptimizationJob operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createPipeline
```
CreatePipelineResult createPipeline(CreatePipelineRequest createPipelineRequest)
```
    Creates a pipeline using a JSON pipeline definition.
    
    Parameters:
    
    createPipelineRequest -
    
    Returns:
    
    Result of the CreatePipeline operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    ConflictException - There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.
    
    See Also:
    
    AWS API Documentation
  - createPresignedDomainUrl
```
CreatePresignedDomainUrlResult createPresignedDomainUrl(CreatePresignedDomainUrlRequest createPresignedDomainUrlRequest)
```
    Creates a URL for a specified UserProfile in a Domain. When accessed in a web browser, the user will be automatically signed in to the domain, and granted access to all of the Apps and files associated with the Domain's Amazon Elastic File System volume. This operation can only be called when the authentication mode equals IAM.
    
    The IAM role or user passed to this API defines the permissions to access the app. Once the presigned URL is created, no additional permission is required to access this URL. IAM authorization policies for this API are also enforced for every HTTP request and WebSocket frame that attempts to connect to the app.
    
    You can restrict access to this API and to the URL that it returns to a list of IP addresses, Amazon VPCs or Amazon VPC Endpoints that you specify. For more information, see Connect to Amazon SageMaker Studio Through an Interface VPC Endpoint .
    
    The URL that you get from a call to CreatePresignedDomainUrl has a default timeout of 5 minutes. You can configure this value using ExpiresInSeconds. If you try to use the URL after the timeout limit expires, you are directed to the Amazon Web Services console sign-in page.
    
    Parameters:
    
    createPresignedDomainUrlRequest -
    
    Returns:
    
    Result of the CreatePresignedDomainUrl operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - createPresignedMlflowTrackingServerUrl
```
CreatePresignedMlflowTrackingServerUrlResult createPresignedMlflowTrackingServerUrl(CreatePresignedMlflowTrackingServerUrlRequest createPresignedMlflowTrackingServerUrlRequest)
```
    Returns a presigned URL that you can use to connect to the MLflow UI attached to your tracking server. For more information, see Launch the MLflow UI using a presigned URL.
    
    Parameters:
    
    createPresignedMlflowTrackingServerUrlRequest -
    
    Returns:
    
    Result of the CreatePresignedMlflowTrackingServerUrl operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - createPresignedNotebookInstanceUrl
```
CreatePresignedNotebookInstanceUrlResult createPresignedNotebookInstanceUrl(CreatePresignedNotebookInstanceUrlRequest createPresignedNotebookInstanceUrlRequest)
```
    Returns a URL that you can use to connect to the Jupyter server from a notebook instance. In the SageMaker console, when you choose Open next to a notebook instance, SageMaker opens a new tab showing the Jupyter server home page from the notebook instance. The console uses this API to get the URL and show the page.
    
    The IAM role or user used to call this API defines the permissions to access the notebook instance. Once the presigned URL is created, no additional permission is required to access this URL. IAM authorization policies for this API are also enforced for every HTTP request and WebSocket frame that attempts to connect to the notebook instance.
    
    You can restrict access to this API and to the URL that it returns to a list of IP addresses that you specify. Use the NotIpAddress condition operator and the aws:SourceIP condition context key to specify the list of IP addresses that you want to have access to the notebook instance. For more information, see Limit Access to a Notebook Instance by IP Address.
    
    The URL that you get from a call to CreatePresignedNotebookInstanceUrl is valid only for 5 minutes. If you try to use the URL after the 5-minute limit expires, you are directed to the Amazon Web Services console sign-in page.
    
    Parameters:
    
    createPresignedNotebookInstanceUrlRequest -
    
    Returns:
    
    Result of the CreatePresignedNotebookInstanceUrl operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - createProcessingJob
```
CreateProcessingJobResult createProcessingJob(CreateProcessingJobRequest createProcessingJobRequest)
```
    Creates a processing job.
    
    Parameters:
    
    createProcessingJobRequest -
    
    Returns:
    
    Result of the CreateProcessingJob operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - createProject
```
CreateProjectResult createProject(CreateProjectRequest createProjectRequest)
```
    Creates a machine learning (ML) project that can contain one or more templates that set up an ML pipeline from training to deploying an approved model.
    
    Parameters:
    
    createProjectRequest -
    
    Returns:
    
    Result of the CreateProject operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createSpace
```
CreateSpaceResult createSpace(CreateSpaceRequest createSpaceRequest)
```
    Creates a private space or a space used for real time collaboration in a domain.
    
    Parameters:
    
    createSpaceRequest -
    
    Returns:
    
    Result of the CreateSpace operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    ResourceInUseException - Resource being accessed is in use.
    
    See Also:
    
    AWS API Documentation
  - createStudioLifecycleConfig
```
CreateStudioLifecycleConfigResult createStudioLifecycleConfig(CreateStudioLifecycleConfigRequest createStudioLifecycleConfigRequest)
```
    Creates a new Amazon SageMaker Studio Lifecycle Configuration.
    
    Parameters:
    
    createStudioLifecycleConfigRequest -
    
    Returns:
    
    Result of the CreateStudioLifecycleConfig operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    See Also:
    
    AWS API Documentation
  - createTrainingJob
```
CreateTrainingJobResult createTrainingJob(CreateTrainingJobRequest createTrainingJobRequest)
```
    Starts a model training job. After training completes, SageMaker saves the resulting model artifacts to an Amazon S3 location that you specify.
    
    If you choose to host your model using SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts in a machine learning service other than SageMaker, provided that you know how to use them for inference.
    
    In the request body, you provide the following:
    - AlgorithmSpecification - Identifies the training algorithm to use.
    - HyperParameters - Specify these algorithm-specific parameters to enable the estimation of model parameters during training. Hyperparameters can be tuned to optimize this learning process. For a list of hyperparameters for each training algorithm provided by SageMaker, see Algorithms.
      
      Do not include any security-sensitive information including account access IDs, secrets or tokens in any hyperparameter field. If the use of security-sensitive credentials are detected, SageMaker will reject your training job request and return an exception error.
    - InputDataConfig - Describes the input required by the training job and the Amazon S3, EFS, or FSx location where it is stored.
    - OutputDataConfig - Identifies the Amazon S3 bucket where you want SageMaker to save the results of model training.
    - ResourceConfig - Identifies the resources, ML compute instances, and ML storage volumes to deploy for model training. In distributed training, you specify more than one instance.
    - EnableManagedSpotTraining - Optimize the cost of training machine learning models by up to 80% by using Amazon EC2 Spot instances. For more information, see Managed Spot Training.
    - RoleArn - The Amazon Resource Name (ARN) that SageMaker assumes to perform tasks on your behalf during model training. You must grant this role the necessary permissions so that SageMaker can successfully complete model training.
    - StoppingCondition - To help cap training costs, use MaxRuntimeInSeconds to set a time limit for training. Use MaxWaitTimeInSeconds to specify how long a managed spot training job has to complete.
    - Environment - The environment variables to set in the Docker container.
    - RetryStrategy - The number of times to retry the job when the job fails due to an InternalServerError.
    For more information about SageMaker, see How It Works.
    Parameters:
    
    createTrainingJobRequest -
    
    Returns:
    
    Result of the CreateTrainingJob operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - createTransformJob
```
CreateTransformJobResult createTransformJob(CreateTransformJobRequest createTransformJobRequest)
```
    Starts a transform job. A transform job uses a trained model to get inferences on a dataset and saves these results to an Amazon S3 location that you specify.
    
    To perform batch transformations, you create a transform job and use the data that you have readily available.
    
    In the request body, you provide the following:
    - TransformJobName - Identifies the transform job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.
    - ModelName - Identifies the model to use. ModelName must be the name of an existing Amazon SageMaker model in the same Amazon Web Services Region and Amazon Web Services account. For information on creating a model, see CreateModel.
    - TransformInput - Describes the dataset to be transformed and the Amazon S3 location where it is stored.
    - TransformOutput - Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.
    - TransformResources - Identifies the ML compute instances for the transform job.
    For more information about how batch transformation works, see Batch Transform.
    Parameters:
    
    createTransformJobRequest -
    
    Returns:
    
    Result of the CreateTransformJob operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - createTrial
```
CreateTrialResult createTrial(CreateTrialRequest createTrialRequest)
```
    Creates an SageMaker trial. A trial is a set of steps called trial components that produce a machine learning model. A trial is part of a single SageMaker experiment.
    
    When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK.
    
    You can add tags to a trial and then use the Search API to search for the tags.
    
    To get a list of all your trials, call the ListTrials API. To view a trial's properties, call the DescribeTrial API. To create a trial component, call the CreateTrialComponent API.
    
    Parameters:
    
    createTrialRequest -
    
    Returns:
    
    Result of the CreateTrial operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createTrialComponent
```
CreateTrialComponentResult createTrialComponent(CreateTrialComponentRequest createTrialComponentRequest)
```
    Creates a trial component, which is a stage of a machine learning trial. A trial is composed of one or more trial components. A trial component can be used in multiple trials.
    
    Trial components include pre-processing jobs, training jobs, and batch transform jobs.
    
    When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK.
    
    You can add tags to a trial component and then use the Search API to search for the tags.
    
    Parameters:
    
    createTrialComponentRequest -
    
    Returns:
    
    Result of the CreateTrialComponent operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createUserProfile
```
CreateUserProfileResult createUserProfile(CreateUserProfileRequest createUserProfileRequest)
```
    Creates a user profile. A user profile represents a single user within a domain, and is the main way to reference a "person" for the purposes of sharing, reporting, and other user-oriented features. This entity is created when a user onboards to a domain. If an administrator invites a person by email or imports them from IAM Identity Center, a user profile is automatically created. A user profile is the primary holder of settings for an individual user and has a reference to the user's private Amazon Elastic File System home directory.
    
    Parameters:
    
    createUserProfileRequest -
    
    Returns:
    
    Result of the CreateUserProfile operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    ResourceInUseException - Resource being accessed is in use.
    
    See Also:
    
    AWS API Documentation
  - createWorkforce
```
CreateWorkforceResult createWorkforce(CreateWorkforceRequest createWorkforceRequest)
```
    Use this operation to create a workforce. This operation will return an error if a workforce already exists in the Amazon Web Services Region that you specify. You can only create one workforce in each Amazon Web Services Region per Amazon Web Services account.
    
    If you want to create a new workforce in an Amazon Web Services Region where a workforce already exists, use the DeleteWorkforce API operation to delete the existing workforce and then use CreateWorkforce to create a new workforce.
    
    To create a private workforce using Amazon Cognito, you must specify a Cognito user pool in CognitoConfig. You can also create an Amazon Cognito workforce using the Amazon SageMaker console. For more information, see Create a Private Workforce (Amazon Cognito).
    
    To create a private workforce using your own OIDC Identity Provider (IdP), specify your IdP configuration in OidcConfig. Your OIDC IdP must support groups because groups are used by Ground Truth and Amazon A2I to create work teams. For more information, see Create a Private Workforce (OIDC IdP).
    
    Parameters:
    
    createWorkforceRequest -
    
    Returns:
    
    Result of the CreateWorkforce operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - createWorkteam
```
CreateWorkteamResult createWorkteam(CreateWorkteamRequest createWorkteamRequest)
```
    Creates a new work team for labeling your data. A work team is defined by one or more Amazon Cognito user pools. You must first create the user pools before you can create a work team.
    
    You cannot create more than 25 work teams in an account and region.
    
    Parameters:
    
    createWorkteamRequest -
    
    Returns:
    
    Result of the CreateWorkteam operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - deleteAction
```
DeleteActionResult deleteAction(DeleteActionRequest deleteActionRequest)
```
    Deletes an action.
    
    Parameters:
    
    deleteActionRequest -
    
    Returns:
    
    Result of the DeleteAction operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteAlgorithm
```
DeleteAlgorithmResult deleteAlgorithm(DeleteAlgorithmRequest deleteAlgorithmRequest)
```
    Removes the specified algorithm from your account.
    
    Parameters:
    
    deleteAlgorithmRequest -
    
    Returns:
    
    Result of the DeleteAlgorithm operation returned by the service.
    
    Throws:
    
    ConflictException - There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.
    
    See Also:
    
    AWS API Documentation
  - deleteApp
```
DeleteAppResult deleteApp(DeleteAppRequest deleteAppRequest)
```
    Used to stop and delete an app.
    
    Parameters:
    
    deleteAppRequest -
    
    Returns:
    
    Result of the DeleteApp operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteAppImageConfig
```
DeleteAppImageConfigResult deleteAppImageConfig(DeleteAppImageConfigRequest deleteAppImageConfigRequest)
```
    Deletes an AppImageConfig.
    
    Parameters:
    
    deleteAppImageConfigRequest -
    
    Returns:
    
    Result of the DeleteAppImageConfig operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteArtifact
```
DeleteArtifactResult deleteArtifact(DeleteArtifactRequest deleteArtifactRequest)
```
    Deletes an artifact. Either ArtifactArn or Source must be specified.
    
    Parameters:
    
    deleteArtifactRequest -
    
    Returns:
    
    Result of the DeleteArtifact operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteAssociation
```
DeleteAssociationResult deleteAssociation(DeleteAssociationRequest deleteAssociationRequest)
```
    Deletes an association.
    
    Parameters:
    
    deleteAssociationRequest -
    
    Returns:
    
    Result of the DeleteAssociation operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteCluster
```
DeleteClusterResult deleteCluster(DeleteClusterRequest deleteClusterRequest)
```
    Delete a SageMaker HyperPod cluster.
    
    Parameters:
    
    deleteClusterRequest -
    
    Returns:
    
    Result of the DeleteCluster operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    ConflictException - There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.
    
    See Also:
    
    AWS API Documentation
  - deleteCodeRepository
```
DeleteCodeRepositoryResult deleteCodeRepository(DeleteCodeRepositoryRequest deleteCodeRepositoryRequest)
```
    Deletes the specified Git repository from your account.
    
    Parameters:
    
    deleteCodeRepositoryRequest -
    
    Returns:
    
    Result of the DeleteCodeRepository operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteCompilationJob
```
DeleteCompilationJobResult deleteCompilationJob(DeleteCompilationJobRequest deleteCompilationJobRequest)
```
    Deletes the specified compilation job. This action deletes only the compilation job resource in Amazon SageMaker. It doesn't delete other resources that are related to that job, such as the model artifacts that the job creates, the compilation logs in CloudWatch, the compiled model, or the IAM role.
    
    You can delete a compilation job only if its current status is COMPLETED, FAILED, or STOPPED. If the job status is STARTING or INPROGRESS, stop the job, and then delete it after its status becomes STOPPED.
    
    Parameters:
    
    deleteCompilationJobRequest -
    
    Returns:
    
    Result of the DeleteCompilationJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteContext
```
DeleteContextResult deleteContext(DeleteContextRequest deleteContextRequest)
```
    Deletes an context.
    
    Parameters:
    
    deleteContextRequest -
    
    Returns:
    
    Result of the DeleteContext operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteDataQualityJobDefinition
```
DeleteDataQualityJobDefinitionResult deleteDataQualityJobDefinition(DeleteDataQualityJobDefinitionRequest deleteDataQualityJobDefinitionRequest)
```
    Deletes a data quality monitoring job definition.
    
    Parameters:
    
    deleteDataQualityJobDefinitionRequest -
    
    Returns:
    
    Result of the DeleteDataQualityJobDefinition operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteDeviceFleet
```
DeleteDeviceFleetResult deleteDeviceFleet(DeleteDeviceFleetRequest deleteDeviceFleetRequest)
```
    Deletes a fleet.
    
    Parameters:
    
    deleteDeviceFleetRequest -
    
    Returns:
    
    Result of the DeleteDeviceFleet operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    See Also:
    
    AWS API Documentation
  - deleteDomain
```
DeleteDomainResult deleteDomain(DeleteDomainRequest deleteDomainRequest)
```
    Used to delete a domain. If you onboarded with IAM mode, you will need to delete your domain to onboard again using IAM Identity Center. Use with caution. All of the members of the domain will lose access to their EFS volume, including data, notebooks, and other artifacts.
    
    Parameters:
    
    deleteDomainRequest -
    
    Returns:
    
    Result of the DeleteDomain operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteEdgeDeploymentPlan
```
DeleteEdgeDeploymentPlanResult deleteEdgeDeploymentPlan(DeleteEdgeDeploymentPlanRequest deleteEdgeDeploymentPlanRequest)
```
    Deletes an edge deployment plan if (and only if) all the stages in the plan are inactive or there are no stages in the plan.
    
    Parameters:
    
    deleteEdgeDeploymentPlanRequest -
    
    Returns:
    
    Result of the DeleteEdgeDeploymentPlan operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    See Also:
    
    AWS API Documentation
  - deleteEdgeDeploymentStage
```
DeleteEdgeDeploymentStageResult deleteEdgeDeploymentStage(DeleteEdgeDeploymentStageRequest deleteEdgeDeploymentStageRequest)
```
    Delete a stage in an edge deployment plan if (and only if) the stage is inactive.
    
    Parameters:
    
    deleteEdgeDeploymentStageRequest -
    
    Returns:
    
    Result of the DeleteEdgeDeploymentStage operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    See Also:
    
    AWS API Documentation
  - deleteEndpoint
```
DeleteEndpointResult deleteEndpoint(DeleteEndpointRequest deleteEndpointRequest)
```
    Deletes an endpoint. SageMaker frees up all of the resources that were deployed when the endpoint was created.
    
    SageMaker retires any custom KMS key grants associated with the endpoint, meaning you don't need to use the RevokeGrant API call.
    
    When you delete your endpoint, SageMaker asynchronously deletes associated endpoint resources such as KMS key grants. You might still see these resources in your account for a few minutes after deleting your endpoint. Do not delete or revoke the permissions for your ExecutionRoleArn , otherwise SageMaker cannot delete these resources.
    
    Parameters:
    
    deleteEndpointRequest -
    
    Returns:
    
    Result of the DeleteEndpoint operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteEndpointConfig
```
DeleteEndpointConfigResult deleteEndpointConfig(DeleteEndpointConfigRequest deleteEndpointConfigRequest)
```
    Deletes an endpoint configuration. The DeleteEndpointConfig API deletes only the specified configuration. It does not delete endpoints created using the configuration.
    
    You must not delete an EndpointConfig in use by an endpoint that is live or while the UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. If you delete the EndpointConfig of an endpoint that is active or being created or updated you may lose visibility into the instance type the endpoint is using. The endpoint must be deleted in order to stop incurring charges.
    
    Parameters:
    
    deleteEndpointConfigRequest -
    
    Returns:
    
    Result of the DeleteEndpointConfig operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteExperiment
```
DeleteExperimentResult deleteExperiment(DeleteExperimentRequest deleteExperimentRequest)
```
    Deletes an SageMaker experiment. All trials associated with the experiment must be deleted first. Use the ListTrials API to get a list of the trials associated with the experiment.
    
    Parameters:
    
    deleteExperimentRequest -
    
    Returns:
    
    Result of the DeleteExperiment operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteFeatureGroup
```
DeleteFeatureGroupResult deleteFeatureGroup(DeleteFeatureGroupRequest deleteFeatureGroupRequest)
```
    Delete the FeatureGroup and any data that was written to the OnlineStore of the FeatureGroup. Data cannot be accessed from the OnlineStore immediately after DeleteFeatureGroup is called.
    
    Data written into the OfflineStore will not be deleted. The Amazon Web Services Glue database and tables that are automatically created for your OfflineStore are not deleted.
    
    Note that it can take approximately 10-15 minutes to delete an OnlineStore FeatureGroup with the InMemory StorageType.
    
    Parameters:
    
    deleteFeatureGroupRequest -
    
    Returns:
    
    Result of the DeleteFeatureGroup operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteFlowDefinition
```
DeleteFlowDefinitionResult deleteFlowDefinition(DeleteFlowDefinitionRequest deleteFlowDefinitionRequest)
```
    Deletes the specified flow definition.
    
    Parameters:
    
    deleteFlowDefinitionRequest -
    
    Returns:
    
    Result of the DeleteFlowDefinition operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteHub
```
DeleteHubResult deleteHub(DeleteHubRequest deleteHubRequest)
```
    Delete a hub.
    
    Parameters:
    
    deleteHubRequest -
    
    Returns:
    
    Result of the DeleteHub operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteHubContent
```
DeleteHubContentResult deleteHubContent(DeleteHubContentRequest deleteHubContentRequest)
```
    Delete the contents of a hub.
    
    Parameters:
    
    deleteHubContentRequest -
    
    Returns:
    
    Result of the DeleteHubContent operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteHubContentReference
```
DeleteHubContentReferenceResult deleteHubContentReference(DeleteHubContentReferenceRequest deleteHubContentReferenceRequest)
```
    Delete a hub content reference in order to remove a model from a private hub.
    
    Parameters:
    
    deleteHubContentReferenceRequest -
    
    Returns:
    
    Result of the DeleteHubContentReference operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteHumanTaskUi
```
DeleteHumanTaskUiResult deleteHumanTaskUi(DeleteHumanTaskUiRequest deleteHumanTaskUiRequest)
```
    Use this operation to delete a human task user interface (worker task template).
    
    To see a list of human task user interfaces (work task templates) in your account, use ListHumanTaskUis. When you delete a worker task template, it no longer appears when you call ListHumanTaskUis.
    
    Parameters:
    
    deleteHumanTaskUiRequest -
    
    Returns:
    
    Result of the DeleteHumanTaskUi operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteHyperParameterTuningJob
```
DeleteHyperParameterTuningJobResult deleteHyperParameterTuningJob(DeleteHyperParameterTuningJobRequest deleteHyperParameterTuningJobRequest)
```
    Deletes a hyperparameter tuning job. The DeleteHyperParameterTuningJob API deletes only the tuning job entry that was created in SageMaker when you called the CreateHyperParameterTuningJob API. It does not delete training jobs, artifacts, or the IAM role that you specified when creating the model.
    
    Parameters:
    
    deleteHyperParameterTuningJobRequest -
    
    Returns:
    
    Result of the DeleteHyperParameterTuningJob operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteImage
```
DeleteImageResult deleteImage(DeleteImageRequest deleteImageRequest)
```
    Deletes a SageMaker image and all versions of the image. The container images aren't deleted.
    
    Parameters:
    
    deleteImageRequest -
    
    Returns:
    
    Result of the DeleteImage operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteImageVersion
```
DeleteImageVersionResult deleteImageVersion(DeleteImageVersionRequest deleteImageVersionRequest)
```
    Deletes a version of a SageMaker image. The container image the version represents isn't deleted.
    
    Parameters:
    
    deleteImageVersionRequest -
    
    Returns:
    
    Result of the DeleteImageVersion operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteInferenceComponent
```
DeleteInferenceComponentResult deleteInferenceComponent(DeleteInferenceComponentRequest deleteInferenceComponentRequest)
```
    Deletes an inference component.
    
    Parameters:
    
    deleteInferenceComponentRequest -
    
    Returns:
    
    Result of the DeleteInferenceComponent operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteInferenceExperiment
```
DeleteInferenceExperimentResult deleteInferenceExperiment(DeleteInferenceExperimentRequest deleteInferenceExperimentRequest)
```
    Deletes an inference experiment.
    
    This operation does not delete your endpoint, variants, or any underlying resources. This operation only deletes the metadata of your experiment.
    
    Parameters:
    
    deleteInferenceExperimentRequest -
    
    Returns:
    
    Result of the DeleteInferenceExperiment operation returned by the service.
    
    Throws:
    
    ConflictException - There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteMlflowTrackingServer
```
DeleteMlflowTrackingServerResult deleteMlflowTrackingServer(DeleteMlflowTrackingServerRequest deleteMlflowTrackingServerRequest)
```
    Deletes an MLflow Tracking Server. For more information, see Clean up MLflow resources.
    
    Parameters:
    
    deleteMlflowTrackingServerRequest -
    
    Returns:
    
    Result of the DeleteMlflowTrackingServer operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteModel
```
DeleteModelResult deleteModel(DeleteModelRequest deleteModelRequest)
```
    Deletes a model. The DeleteModel API deletes only the model entry that was created in SageMaker when you called the CreateModel API. It does not delete model artifacts, inference code, or the IAM role that you specified when creating the model.
    
    Parameters:
    
    deleteModelRequest -
    
    Returns:
    
    Result of the DeleteModel operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteModelBiasJobDefinition
```
DeleteModelBiasJobDefinitionResult deleteModelBiasJobDefinition(DeleteModelBiasJobDefinitionRequest deleteModelBiasJobDefinitionRequest)
```
    Deletes an Amazon SageMaker model bias job definition.
    
    Parameters:
    
    deleteModelBiasJobDefinitionRequest -
    
    Returns:
    
    Result of the DeleteModelBiasJobDefinition operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteModelCard
```
DeleteModelCardResult deleteModelCard(DeleteModelCardRequest deleteModelCardRequest)
```
    Deletes an Amazon SageMaker Model Card.
    
    Parameters:
    
    deleteModelCardRequest -
    
    Returns:
    
    Result of the DeleteModelCard operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    ConflictException - There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.
    
    See Also:
    
    AWS API Documentation
  - deleteModelExplainabilityJobDefinition
```
DeleteModelExplainabilityJobDefinitionResult deleteModelExplainabilityJobDefinition(DeleteModelExplainabilityJobDefinitionRequest deleteModelExplainabilityJobDefinitionRequest)
```
    Deletes an Amazon SageMaker model explainability job definition.
    
    Parameters:
    
    deleteModelExplainabilityJobDefinitionRequest -
    
    Returns:
    
    Result of the DeleteModelExplainabilityJobDefinition operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteModelPackage
```
DeleteModelPackageResult deleteModelPackage(DeleteModelPackageRequest deleteModelPackageRequest)
```
    Deletes a model package.
    
    A model package is used to create SageMaker models or list on Amazon Web Services Marketplace. Buyers can subscribe to model packages listed on Amazon Web Services Marketplace to create models in SageMaker.
    
    Parameters:
    
    deleteModelPackageRequest -
    
    Returns:
    
    Result of the DeleteModelPackage operation returned by the service.
    
    Throws:
    
    ConflictException - There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.
    
    See Also:
    
    AWS API Documentation
  - deleteModelPackageGroup
```
DeleteModelPackageGroupResult deleteModelPackageGroup(DeleteModelPackageGroupRequest deleteModelPackageGroupRequest)
```
    Deletes the specified model group.
    
    Parameters:
    
    deleteModelPackageGroupRequest -
    
    Returns:
    
    Result of the DeleteModelPackageGroup operation returned by the service.
    
    Throws:
    
    ConflictException - There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.
    
    See Also:
    
    AWS API Documentation
  - deleteModelPackageGroupPolicy
```
DeleteModelPackageGroupPolicyResult deleteModelPackageGroupPolicy(DeleteModelPackageGroupPolicyRequest deleteModelPackageGroupPolicyRequest)
```
    Deletes a model group resource policy.
    
    Parameters:
    
    deleteModelPackageGroupPolicyRequest -
    
    Returns:
    
    Result of the DeleteModelPackageGroupPolicy operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteModelQualityJobDefinition
```
DeleteModelQualityJobDefinitionResult deleteModelQualityJobDefinition(DeleteModelQualityJobDefinitionRequest deleteModelQualityJobDefinitionRequest)
```
    Deletes the secified model quality monitoring job definition.
    
    Parameters:
    
    deleteModelQualityJobDefinitionRequest -
    
    Returns:
    
    Result of the DeleteModelQualityJobDefinition operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteMonitoringSchedule
```
DeleteMonitoringScheduleResult deleteMonitoringSchedule(DeleteMonitoringScheduleRequest deleteMonitoringScheduleRequest)
```
    Deletes a monitoring schedule. Also stops the schedule had not already been stopped. This does not delete the job execution history of the monitoring schedule.
    
    Parameters:
    
    deleteMonitoringScheduleRequest -
    
    Returns:
    
    Result of the DeleteMonitoringSchedule operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteNotebookInstance
```
DeleteNotebookInstanceResult deleteNotebookInstance(DeleteNotebookInstanceRequest deleteNotebookInstanceRequest)
```
    Deletes an SageMaker notebook instance. Before you can delete a notebook instance, you must call the StopNotebookInstance API.
    
    When you delete a notebook instance, you lose all of your data. SageMaker removes the ML compute instance, and deletes the ML storage volume and the network interface associated with the notebook instance.
    
    Parameters:
    
    deleteNotebookInstanceRequest -
    
    Returns:
    
    Result of the DeleteNotebookInstance operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteNotebookInstanceLifecycleConfig
```
DeleteNotebookInstanceLifecycleConfigResult deleteNotebookInstanceLifecycleConfig(DeleteNotebookInstanceLifecycleConfigRequest deleteNotebookInstanceLifecycleConfigRequest)
```
    Deletes a notebook instance lifecycle configuration.
    
    Parameters:
    
    deleteNotebookInstanceLifecycleConfigRequest -
    
    Returns:
    
    Result of the DeleteNotebookInstanceLifecycleConfig operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteOptimizationJob
```
DeleteOptimizationJobResult deleteOptimizationJob(DeleteOptimizationJobRequest deleteOptimizationJobRequest)
```
    Deletes an optimization job.
    
    Parameters:
    
    deleteOptimizationJobRequest -
    
    Returns:
    
    Result of the DeleteOptimizationJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deletePipeline
```
DeletePipelineResult deletePipeline(DeletePipelineRequest deletePipelineRequest)
```
    Deletes a pipeline if there are no running instances of the pipeline. To delete a pipeline, you must stop all running instances of the pipeline using the StopPipelineExecution API. When you delete a pipeline, all instances of the pipeline are deleted.
    
    Parameters:
    
    deletePipelineRequest -
    
    Returns:
    
    Result of the DeletePipeline operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    ConflictException - There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.
    
    See Also:
    
    AWS API Documentation
  - deleteProject
```
DeleteProjectResult deleteProject(DeleteProjectRequest deleteProjectRequest)
```
    Delete the specified project.
    
    Parameters:
    
    deleteProjectRequest -
    
    Returns:
    
    Result of the DeleteProject operation returned by the service.
    
    Throws:
    
    ConflictException - There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.
    
    See Also:
    
    AWS API Documentation
  - deleteSpace
```
DeleteSpaceResult deleteSpace(DeleteSpaceRequest deleteSpaceRequest)
```
    Used to delete a space.
    
    Parameters:
    
    deleteSpaceRequest -
    
    Returns:
    
    Result of the DeleteSpace operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteStudioLifecycleConfig
```
DeleteStudioLifecycleConfigResult deleteStudioLifecycleConfig(DeleteStudioLifecycleConfigRequest deleteStudioLifecycleConfigRequest)
```
    Deletes the Amazon SageMaker Studio Lifecycle Configuration. In order to delete the Lifecycle Configuration, there must be no running apps using the Lifecycle Configuration. You must also remove the Lifecycle Configuration from UserSettings in all Domains and UserProfiles.
    
    Parameters:
    
    deleteStudioLifecycleConfigRequest -
    
    Returns:
    
    Result of the DeleteStudioLifecycleConfig operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    ResourceInUseException - Resource being accessed is in use.
    
    See Also:
    
    AWS API Documentation
  - deleteTags
```
DeleteTagsResult deleteTags(DeleteTagsRequest deleteTagsRequest)
```
    Deletes the specified tags from an SageMaker resource.
    
    To list a resource's tags, use the ListTags API.
    
    When you call this API to delete tags from a hyperparameter tuning job, the deleted tags are not removed from training jobs that the hyperparameter tuning job launched before you called this API.
    
    When you call this API to delete tags from a SageMaker Domain or User Profile, the deleted tags are not removed from Apps that the SageMaker Domain or User Profile launched before you called this API.
    
    Parameters:
    
    deleteTagsRequest -
    
    Returns:
    
    Result of the DeleteTags operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteTrial
```
DeleteTrialResult deleteTrial(DeleteTrialRequest deleteTrialRequest)
```
    Deletes the specified trial. All trial components that make up the trial must be deleted first. Use the DescribeTrialComponent API to get the list of trial components.
    
    Parameters:
    
    deleteTrialRequest -
    
    Returns:
    
    Result of the DeleteTrial operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteTrialComponent
```
DeleteTrialComponentResult deleteTrialComponent(DeleteTrialComponentRequest deleteTrialComponentRequest)
```
    Deletes the specified trial component. A trial component must be disassociated from all trials before the trial component can be deleted. To disassociate a trial component from a trial, call the DisassociateTrialComponent API.
    
    Parameters:
    
    deleteTrialComponentRequest -
    
    Returns:
    
    Result of the DeleteTrialComponent operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteUserProfile
```
DeleteUserProfileResult deleteUserProfile(DeleteUserProfileRequest deleteUserProfileRequest)
```
    Deletes a user profile. When a user profile is deleted, the user loses access to their EFS volume, including data, notebooks, and other artifacts.
    
    Parameters:
    
    deleteUserProfileRequest -
    
    Returns:
    
    Result of the DeleteUserProfile operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteWorkforce
```
DeleteWorkforceResult deleteWorkforce(DeleteWorkforceRequest deleteWorkforceRequest)
```
    Use this operation to delete a workforce.
    
    If you want to create a new workforce in an Amazon Web Services Region where a workforce already exists, use this operation to delete the existing workforce and then use CreateWorkforce to create a new workforce.
    
    If a private workforce contains one or more work teams, you must use the DeleteWorkteam operation to delete all work teams before you delete the workforce. If you try to delete a workforce that contains one or more work teams, you will receive a ResourceInUse error.
    
    Parameters:
    
    deleteWorkforceRequest -
    
    Returns:
    
    Result of the DeleteWorkforce operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteWorkteam
```
DeleteWorkteamResult deleteWorkteam(DeleteWorkteamRequest deleteWorkteamRequest)
```
    Deletes an existing work team. This operation can't be undone.
    
    Parameters:
    
    deleteWorkteamRequest -
    
    Returns:
    
    Result of the DeleteWorkteam operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - deregisterDevices
```
DeregisterDevicesResult deregisterDevices(DeregisterDevicesRequest deregisterDevicesRequest)
```
    Deregisters the specified devices. After you deregister a device, you will need to re-register the devices.
    
    Parameters:
    
    deregisterDevicesRequest -
    
    Returns:
    
    Result of the DeregisterDevices operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeAction
```
DescribeActionResult describeAction(DescribeActionRequest describeActionRequest)
```
    Describes an action.
    
    Parameters:
    
    describeActionRequest -
    
    Returns:
    
    Result of the DescribeAction operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeAlgorithm
```
DescribeAlgorithmResult describeAlgorithm(DescribeAlgorithmRequest describeAlgorithmRequest)
```
    Returns a description of the specified algorithm that is in your account.
    
    Parameters:
    
    describeAlgorithmRequest -
    
    Returns:
    
    Result of the DescribeAlgorithm operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeApp
```
DescribeAppResult describeApp(DescribeAppRequest describeAppRequest)
```
    Describes the app.
    
    Parameters:
    
    describeAppRequest -
    
    Returns:
    
    Result of the DescribeApp operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeAppImageConfig
```
DescribeAppImageConfigResult describeAppImageConfig(DescribeAppImageConfigRequest describeAppImageConfigRequest)
```
    Describes an AppImageConfig.
    
    Parameters:
    
    describeAppImageConfigRequest -
    
    Returns:
    
    Result of the DescribeAppImageConfig operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeArtifact
```
DescribeArtifactResult describeArtifact(DescribeArtifactRequest describeArtifactRequest)
```
    Describes an artifact.
    
    Parameters:
    
    describeArtifactRequest -
    
    Returns:
    
    Result of the DescribeArtifact operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeAutoMLJob
```
DescribeAutoMLJobResult describeAutoMLJob(DescribeAutoMLJobRequest describeAutoMLJobRequest)
```
    Returns information about an AutoML job created by calling CreateAutoMLJob.
    
    AutoML jobs created by calling CreateAutoMLJobV2 cannot be described by DescribeAutoMLJob.
    
    Parameters:
    
    describeAutoMLJobRequest -
    
    Returns:
    
    Result of the DescribeAutoMLJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeAutoMLJobV2
```
DescribeAutoMLJobV2Result describeAutoMLJobV2(DescribeAutoMLJobV2Request describeAutoMLJobV2Request)
```
    Returns information about an AutoML job created by calling CreateAutoMLJobV2 or CreateAutoMLJob.
    
    Parameters:
    
    describeAutoMLJobV2Request -
    
    Returns:
    
    Result of the DescribeAutoMLJobV2 operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeCluster
```
DescribeClusterResult describeCluster(DescribeClusterRequest describeClusterRequest)
```
    Retrieves information of a SageMaker HyperPod cluster.
    
    Parameters:
    
    describeClusterRequest -
    
    Returns:
    
    Result of the DescribeCluster operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeClusterNode
```
DescribeClusterNodeResult describeClusterNode(DescribeClusterNodeRequest describeClusterNodeRequest)
```
    Retrieves information of a node (also called a instance interchangeably) of a SageMaker HyperPod cluster.
    
    Parameters:
    
    describeClusterNodeRequest -
    
    Returns:
    
    Result of the DescribeClusterNode operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeCodeRepository
```
DescribeCodeRepositoryResult describeCodeRepository(DescribeCodeRepositoryRequest describeCodeRepositoryRequest)
```
    Gets details about the specified Git repository.
    
    Parameters:
    
    describeCodeRepositoryRequest -
    
    Returns:
    
    Result of the DescribeCodeRepository operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeCompilationJob
```
DescribeCompilationJobResult describeCompilationJob(DescribeCompilationJobRequest describeCompilationJobRequest)
```
    Returns information about a model compilation job.
    
    To create a model compilation job, use CreateCompilationJob. To get information about multiple model compilation jobs, use ListCompilationJobs.
    
    Parameters:
    
    describeCompilationJobRequest -
    
    Returns:
    
    Result of the DescribeCompilationJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeContext
```
DescribeContextResult describeContext(DescribeContextRequest describeContextRequest)
```
    Describes a context.
    
    Parameters:
    
    describeContextRequest -
    
    Returns:
    
    Result of the DescribeContext operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeDataQualityJobDefinition
```
DescribeDataQualityJobDefinitionResult describeDataQualityJobDefinition(DescribeDataQualityJobDefinitionRequest describeDataQualityJobDefinitionRequest)
```
    Gets the details of a data quality monitoring job definition.
    
    Parameters:
    
    describeDataQualityJobDefinitionRequest -
    
    Returns:
    
    Result of the DescribeDataQualityJobDefinition operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeDevice
```
DescribeDeviceResult describeDevice(DescribeDeviceRequest describeDeviceRequest)
```
    Describes the device.
    
    Parameters:
    
    describeDeviceRequest -
    
    Returns:
    
    Result of the DescribeDevice operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeDeviceFleet
```
DescribeDeviceFleetResult describeDeviceFleet(DescribeDeviceFleetRequest describeDeviceFleetRequest)
```
    A description of the fleet the device belongs to.
    
    Parameters:
    
    describeDeviceFleetRequest -
    
    Returns:
    
    Result of the DescribeDeviceFleet operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeDomain
```
DescribeDomainResult describeDomain(DescribeDomainRequest describeDomainRequest)
```
    The description of the domain.
    
    Parameters:
    
    describeDomainRequest -
    
    Returns:
    
    Result of the DescribeDomain operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeEdgeDeploymentPlan
```
DescribeEdgeDeploymentPlanResult describeEdgeDeploymentPlan(DescribeEdgeDeploymentPlanRequest describeEdgeDeploymentPlanRequest)
```
    Describes an edge deployment plan with deployment status per stage.
    
    Parameters:
    
    describeEdgeDeploymentPlanRequest -
    
    Returns:
    
    Result of the DescribeEdgeDeploymentPlan operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeEdgePackagingJob
```
DescribeEdgePackagingJobResult describeEdgePackagingJob(DescribeEdgePackagingJobRequest describeEdgePackagingJobRequest)
```
    A description of edge packaging jobs.
    
    Parameters:
    
    describeEdgePackagingJobRequest -
    
    Returns:
    
    Result of the DescribeEdgePackagingJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeEndpoint
```
DescribeEndpointResult describeEndpoint(DescribeEndpointRequest describeEndpointRequest)
```
    Returns the description of an endpoint.
    
    Parameters:
    
    describeEndpointRequest -
    
    Returns:
    
    Result of the DescribeEndpoint operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeEndpointConfig
```
DescribeEndpointConfigResult describeEndpointConfig(DescribeEndpointConfigRequest describeEndpointConfigRequest)
```
    Returns the description of an endpoint configuration created using the CreateEndpointConfig API.
    
    Parameters:
    
    describeEndpointConfigRequest -
    
    Returns:
    
    Result of the DescribeEndpointConfig operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeExperiment
```
DescribeExperimentResult describeExperiment(DescribeExperimentRequest describeExperimentRequest)
```
    Provides a list of an experiment's properties.
    
    Parameters:
    
    describeExperimentRequest -
    
    Returns:
    
    Result of the DescribeExperiment operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeFeatureGroup
```
DescribeFeatureGroupResult describeFeatureGroup(DescribeFeatureGroupRequest describeFeatureGroupRequest)
```
    Use this operation to describe a FeatureGroup. The response includes information on the creation time, FeatureGroup name, the unique identifier for each FeatureGroup, and more.
    
    Parameters:
    
    describeFeatureGroupRequest -
    
    Returns:
    
    Result of the DescribeFeatureGroup operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeFeatureMetadata
```
DescribeFeatureMetadataResult describeFeatureMetadata(DescribeFeatureMetadataRequest describeFeatureMetadataRequest)
```
    Shows the metadata for a feature within a feature group.
    
    Parameters:
    
    describeFeatureMetadataRequest -
    
    Returns:
    
    Result of the DescribeFeatureMetadata operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeFlowDefinition
```
DescribeFlowDefinitionResult describeFlowDefinition(DescribeFlowDefinitionRequest describeFlowDefinitionRequest)
```
    Returns information about the specified flow definition.
    
    Parameters:
    
    describeFlowDefinitionRequest -
    
    Returns:
    
    Result of the DescribeFlowDefinition operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeHub
```
DescribeHubResult describeHub(DescribeHubRequest describeHubRequest)
```
    Describes a hub.
    
    Parameters:
    
    describeHubRequest -
    
    Returns:
    
    Result of the DescribeHub operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeHubContent
```
DescribeHubContentResult describeHubContent(DescribeHubContentRequest describeHubContentRequest)
```
    Describe the content of a hub.
    
    Parameters:
    
    describeHubContentRequest -
    
    Returns:
    
    Result of the DescribeHubContent operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeHumanTaskUi
```
DescribeHumanTaskUiResult describeHumanTaskUi(DescribeHumanTaskUiRequest describeHumanTaskUiRequest)
```
    Returns information about the requested human task user interface (worker task template).
    
    Parameters:
    
    describeHumanTaskUiRequest -
    
    Returns:
    
    Result of the DescribeHumanTaskUi operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeHyperParameterTuningJob
```
DescribeHyperParameterTuningJobResult describeHyperParameterTuningJob(DescribeHyperParameterTuningJobRequest describeHyperParameterTuningJobRequest)
```
    Returns a description of a hyperparameter tuning job, depending on the fields selected. These fields can include the name, Amazon Resource Name (ARN), job status of your tuning job and more.
    
    Parameters:
    
    describeHyperParameterTuningJobRequest -
    
    Returns:
    
    Result of the DescribeHyperParameterTuningJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeImage
```
DescribeImageResult describeImage(DescribeImageRequest describeImageRequest)
```
    Describes a SageMaker image.
    
    Parameters:
    
    describeImageRequest -
    
    Returns:
    
    Result of the DescribeImage operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeImageVersion
```
DescribeImageVersionResult describeImageVersion(DescribeImageVersionRequest describeImageVersionRequest)
```
    Describes a version of a SageMaker image.
    
    Parameters:
    
    describeImageVersionRequest -
    
    Returns:
    
    Result of the DescribeImageVersion operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeInferenceComponent
```
DescribeInferenceComponentResult describeInferenceComponent(DescribeInferenceComponentRequest describeInferenceComponentRequest)
```
    Returns information about an inference component.
    
    Parameters:
    
    describeInferenceComponentRequest -
    
    Returns:
    
    Result of the DescribeInferenceComponent operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeInferenceExperiment
```
DescribeInferenceExperimentResult describeInferenceExperiment(DescribeInferenceExperimentRequest describeInferenceExperimentRequest)
```
    Returns details about an inference experiment.
    
    Parameters:
    
    describeInferenceExperimentRequest -
    
    Returns:
    
    Result of the DescribeInferenceExperiment operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeInferenceRecommendationsJob
```
DescribeInferenceRecommendationsJobResult describeInferenceRecommendationsJob(DescribeInferenceRecommendationsJobRequest describeInferenceRecommendationsJobRequest)
```
    Provides the results of the Inference Recommender job. One or more recommendation jobs are returned.
    
    Parameters:
    
    describeInferenceRecommendationsJobRequest -
    
    Returns:
    
    Result of the DescribeInferenceRecommendationsJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeLabelingJob
```
DescribeLabelingJobResult describeLabelingJob(DescribeLabelingJobRequest describeLabelingJobRequest)
```
    Gets information about a labeling job.
    
    Parameters:
    
    describeLabelingJobRequest -
    
    Returns:
    
    Result of the DescribeLabelingJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeLineageGroup
```
DescribeLineageGroupResult describeLineageGroup(DescribeLineageGroupRequest describeLineageGroupRequest)
```
    Provides a list of properties for the requested lineage group. For more information, see Cross-Account Lineage Tracking in the Amazon SageMaker Developer Guide.
    
    Parameters:
    
    describeLineageGroupRequest -
    
    Returns:
    
    Result of the DescribeLineageGroup operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeMlflowTrackingServer
```
DescribeMlflowTrackingServerResult describeMlflowTrackingServer(DescribeMlflowTrackingServerRequest describeMlflowTrackingServerRequest)
```
    Returns information about an MLflow Tracking Server.
    
    Parameters:
    
    describeMlflowTrackingServerRequest -
    
    Returns:
    
    Result of the DescribeMlflowTrackingServer operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeModel
```
DescribeModelResult describeModel(DescribeModelRequest describeModelRequest)
```
    Describes a model that you created using the CreateModel API.
    
    Parameters:
    
    describeModelRequest -
    
    Returns:
    
    Result of the DescribeModel operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeModelBiasJobDefinition
```
DescribeModelBiasJobDefinitionResult describeModelBiasJobDefinition(DescribeModelBiasJobDefinitionRequest describeModelBiasJobDefinitionRequest)
```
    Returns a description of a model bias job definition.
    
    Parameters:
    
    describeModelBiasJobDefinitionRequest -
    
    Returns:
    
    Result of the DescribeModelBiasJobDefinition operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeModelCard
```
DescribeModelCardResult describeModelCard(DescribeModelCardRequest describeModelCardRequest)
```
    Describes the content, creation time, and security configuration of an Amazon SageMaker Model Card.
    
    Parameters:
    
    describeModelCardRequest -
    
    Returns:
    
    Result of the DescribeModelCard operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeModelCardExportJob
```
DescribeModelCardExportJobResult describeModelCardExportJob(DescribeModelCardExportJobRequest describeModelCardExportJobRequest)
```
    Describes an Amazon SageMaker Model Card export job.
    
    Parameters:
    
    describeModelCardExportJobRequest -
    
    Returns:
    
    Result of the DescribeModelCardExportJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeModelExplainabilityJobDefinition
```
DescribeModelExplainabilityJobDefinitionResult describeModelExplainabilityJobDefinition(DescribeModelExplainabilityJobDefinitionRequest describeModelExplainabilityJobDefinitionRequest)
```
    Returns a description of a model explainability job definition.
    
    Parameters:
    
    describeModelExplainabilityJobDefinitionRequest -
    
    Returns:
    
    Result of the DescribeModelExplainabilityJobDefinition operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeModelPackage
```
DescribeModelPackageResult describeModelPackage(DescribeModelPackageRequest describeModelPackageRequest)
```
    Returns a description of the specified model package, which is used to create SageMaker models or list them on Amazon Web Services Marketplace.
    
    If you provided a KMS Key ID when you created your model package, you will see the KMS Decrypt API call in your CloudTrail logs when you use this API.
    
    To create models in SageMaker, buyers can subscribe to model packages listed on Amazon Web Services Marketplace.
    
    Parameters:
    
    describeModelPackageRequest -
    
    Returns:
    
    Result of the DescribeModelPackage operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeModelPackageGroup
```
DescribeModelPackageGroupResult describeModelPackageGroup(DescribeModelPackageGroupRequest describeModelPackageGroupRequest)
```
    Gets a description for the specified model group.
    
    Parameters:
    
    describeModelPackageGroupRequest -
    
    Returns:
    
    Result of the DescribeModelPackageGroup operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeModelQualityJobDefinition
```
DescribeModelQualityJobDefinitionResult describeModelQualityJobDefinition(DescribeModelQualityJobDefinitionRequest describeModelQualityJobDefinitionRequest)
```
    Returns a description of a model quality job definition.
    
    Parameters:
    
    describeModelQualityJobDefinitionRequest -
    
    Returns:
    
    Result of the DescribeModelQualityJobDefinition operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeMonitoringSchedule
```
DescribeMonitoringScheduleResult describeMonitoringSchedule(DescribeMonitoringScheduleRequest describeMonitoringScheduleRequest)
```
    Describes the schedule for a monitoring job.
    
    Parameters:
    
    describeMonitoringScheduleRequest -
    
    Returns:
    
    Result of the DescribeMonitoringSchedule operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeNotebookInstance
```
DescribeNotebookInstanceResult describeNotebookInstance(DescribeNotebookInstanceRequest describeNotebookInstanceRequest)
```
    Returns information about a notebook instance.
    
    Parameters:
    
    describeNotebookInstanceRequest -
    
    Returns:
    
    Result of the DescribeNotebookInstance operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeNotebookInstanceLifecycleConfig
```
DescribeNotebookInstanceLifecycleConfigResult describeNotebookInstanceLifecycleConfig(DescribeNotebookInstanceLifecycleConfigRequest describeNotebookInstanceLifecycleConfigRequest)
```
    Returns a description of a notebook instance lifecycle configuration.
    
    For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.
    
    Parameters:
    
    describeNotebookInstanceLifecycleConfigRequest -
    
    Returns:
    
    Result of the DescribeNotebookInstanceLifecycleConfig operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeOptimizationJob
```
DescribeOptimizationJobResult describeOptimizationJob(DescribeOptimizationJobRequest describeOptimizationJobRequest)
```
    Provides the properties of the specified optimization job.
    
    Parameters:
    
    describeOptimizationJobRequest -
    
    Returns:
    
    Result of the DescribeOptimizationJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describePipeline
```
DescribePipelineResult describePipeline(DescribePipelineRequest describePipelineRequest)
```
    Describes the details of a pipeline.
    
    Parameters:
    
    describePipelineRequest -
    
    Returns:
    
    Result of the DescribePipeline operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describePipelineDefinitionForExecution
```
DescribePipelineDefinitionForExecutionResult describePipelineDefinitionForExecution(DescribePipelineDefinitionForExecutionRequest describePipelineDefinitionForExecutionRequest)
```
    Describes the details of an execution's pipeline definition.
    
    Parameters:
    
    describePipelineDefinitionForExecutionRequest -
    
    Returns:
    
    Result of the DescribePipelineDefinitionForExecution operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describePipelineExecution
```
DescribePipelineExecutionResult describePipelineExecution(DescribePipelineExecutionRequest describePipelineExecutionRequest)
```
    Describes the details of a pipeline execution.
    
    Parameters:
    
    describePipelineExecutionRequest -
    
    Returns:
    
    Result of the DescribePipelineExecution operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeProcessingJob
```
DescribeProcessingJobResult describeProcessingJob(DescribeProcessingJobRequest describeProcessingJobRequest)
```
    Returns a description of a processing job.
    
    Parameters:
    
    describeProcessingJobRequest -
    
    Returns:
    
    Result of the DescribeProcessingJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeProject
```
DescribeProjectResult describeProject(DescribeProjectRequest describeProjectRequest)
```
    Describes the details of a project.
    
    Parameters:
    
    describeProjectRequest -
    
    Returns:
    
    Result of the DescribeProject operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeSpace
```
DescribeSpaceResult describeSpace(DescribeSpaceRequest describeSpaceRequest)
```
    Describes the space.
    
    Parameters:
    
    describeSpaceRequest -
    
    Returns:
    
    Result of the DescribeSpace operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeStudioLifecycleConfig
```
DescribeStudioLifecycleConfigResult describeStudioLifecycleConfig(DescribeStudioLifecycleConfigRequest describeStudioLifecycleConfigRequest)
```
    Describes the Amazon SageMaker Studio Lifecycle Configuration.
    
    Parameters:
    
    describeStudioLifecycleConfigRequest -
    
    Returns:
    
    Result of the DescribeStudioLifecycleConfig operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeSubscribedWorkteam
```
DescribeSubscribedWorkteamResult describeSubscribedWorkteam(DescribeSubscribedWorkteamRequest describeSubscribedWorkteamRequest)
```
    Gets information about a work team provided by a vendor. It returns details about the subscription with a vendor in the Amazon Web Services Marketplace.
    
    Parameters:
    
    describeSubscribedWorkteamRequest -
    
    Returns:
    
    Result of the DescribeSubscribedWorkteam operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeTrainingJob
```
DescribeTrainingJobResult describeTrainingJob(DescribeTrainingJobRequest describeTrainingJobRequest)
```
    Returns information about a training job.
    
    Some of the attributes below only appear if the training job successfully starts. If the training job fails, TrainingJobStatus is Failed and, depending on the FailureReason, attributes like TrainingStartTime, TrainingTimeInSeconds, TrainingEndTime, and BillableTimeInSeconds may not be present in the response.
    
    Parameters:
    
    describeTrainingJobRequest -
    
    Returns:
    
    Result of the DescribeTrainingJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeTransformJob
```
DescribeTransformJobResult describeTransformJob(DescribeTransformJobRequest describeTransformJobRequest)
```
    Returns information about a transform job.
    
    Parameters:
    
    describeTransformJobRequest -
    
    Returns:
    
    Result of the DescribeTransformJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeTrial
```
DescribeTrialResult describeTrial(DescribeTrialRequest describeTrialRequest)
```
    Provides a list of a trial's properties.
    
    Parameters:
    
    describeTrialRequest -
    
    Returns:
    
    Result of the DescribeTrial operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeTrialComponent
```
DescribeTrialComponentResult describeTrialComponent(DescribeTrialComponentRequest describeTrialComponentRequest)
```
    Provides a list of a trials component's properties.
    
    Parameters:
    
    describeTrialComponentRequest -
    
    Returns:
    
    Result of the DescribeTrialComponent operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeUserProfile
```
DescribeUserProfileResult describeUserProfile(DescribeUserProfileRequest describeUserProfileRequest)
```
    Describes a user profile. For more information, see CreateUserProfile.
    
    Parameters:
    
    describeUserProfileRequest -
    
    Returns:
    
    Result of the DescribeUserProfile operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - describeWorkforce
```
DescribeWorkforceResult describeWorkforce(DescribeWorkforceRequest describeWorkforceRequest)
```
    Lists private workforce information, including workforce name, Amazon Resource Name (ARN), and, if applicable, allowed IP address ranges (CIDRs). Allowable IP address ranges are the IP addresses that workers can use to access tasks.
    
    This operation applies only to private workforces.
    
    Parameters:
    
    describeWorkforceRequest -
    
    Returns:
    
    Result of the DescribeWorkforce operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeWorkteam
```
DescribeWorkteamResult describeWorkteam(DescribeWorkteamRequest describeWorkteamRequest)
```
    Gets information about a specific work team. You can see information such as the creation date, the last updated date, membership information, and the work team's Amazon Resource Name (ARN).
    
    Parameters:
    
    describeWorkteamRequest -
    
    Returns:
    
    Result of the DescribeWorkteam operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - disableSagemakerServicecatalogPortfolio
```
DisableSagemakerServicecatalogPortfolioResult disableSagemakerServicecatalogPortfolio(DisableSagemakerServicecatalogPortfolioRequest disableSagemakerServicecatalogPortfolioRequest)
```
    Disables using Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.
    
    Parameters:
    
    disableSagemakerServicecatalogPortfolioRequest -
    
    Returns:
    
    Result of the DisableSagemakerServicecatalogPortfolio operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - disassociateTrialComponent
```
DisassociateTrialComponentResult disassociateTrialComponent(DisassociateTrialComponentRequest disassociateTrialComponentRequest)
```
    Disassociates a trial component from a trial. This doesn't effect other trials the component is associated with. Before you can delete a component, you must disassociate the component from all trials it is associated with. To associate a trial component with a trial, call the AssociateTrialComponent API.
    
    To get a list of the trials a component is associated with, use the Search API. Specify ExperimentTrialComponent for the Resource parameter. The list appears in the response under Results.TrialComponent.Parents.
    
    Parameters:
    
    disassociateTrialComponentRequest -
    
    Returns:
    
    Result of the DisassociateTrialComponent operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - enableSagemakerServicecatalogPortfolio
```
EnableSagemakerServicecatalogPortfolioResult enableSagemakerServicecatalogPortfolio(EnableSagemakerServicecatalogPortfolioRequest enableSagemakerServicecatalogPortfolioRequest)
```
    Enables using Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.
    
    Parameters:
    
    enableSagemakerServicecatalogPortfolioRequest -
    
    Returns:
    
    Result of the EnableSagemakerServicecatalogPortfolio operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - getDeviceFleetReport
```
GetDeviceFleetReportResult getDeviceFleetReport(GetDeviceFleetReportRequest getDeviceFleetReportRequest)
```
    Describes a fleet.
    
    Parameters:
    
    getDeviceFleetReportRequest -
    
    Returns:
    
    Result of the GetDeviceFleetReport operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - getLineageGroupPolicy
```
GetLineageGroupPolicyResult getLineageGroupPolicy(GetLineageGroupPolicyRequest getLineageGroupPolicyRequest)
```
    The resource policy for the lineage group.
    
    Parameters:
    
    getLineageGroupPolicyRequest -
    
    Returns:
    
    Result of the GetLineageGroupPolicy operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - getModelPackageGroupPolicy
```
GetModelPackageGroupPolicyResult getModelPackageGroupPolicy(GetModelPackageGroupPolicyRequest getModelPackageGroupPolicyRequest)
```
    Gets a resource policy that manages access for a model group. For information about resource policies, see Identity-based policies and resource-based policies in the Amazon Web Services Identity and Access Management User Guide..
    
    Parameters:
    
    getModelPackageGroupPolicyRequest -
    
    Returns:
    
    Result of the GetModelPackageGroupPolicy operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - getSagemakerServicecatalogPortfolioStatus
```
GetSagemakerServicecatalogPortfolioStatusResult getSagemakerServicecatalogPortfolioStatus(GetSagemakerServicecatalogPortfolioStatusRequest getSagemakerServicecatalogPortfolioStatusRequest)
```
    Gets the status of Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.
    
    Parameters:
    
    getSagemakerServicecatalogPortfolioStatusRequest -
    
    Returns:
    
    Result of the GetSagemakerServicecatalogPortfolioStatus operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - getScalingConfigurationRecommendation
```
GetScalingConfigurationRecommendationResult getScalingConfigurationRecommendation(GetScalingConfigurationRecommendationRequest getScalingConfigurationRecommendationRequest)
```
    Starts an Amazon SageMaker Inference Recommender autoscaling recommendation job. Returns recommendations for autoscaling policies that you can apply to your SageMaker endpoint.
    
    Parameters:
    
    getScalingConfigurationRecommendationRequest -
    
    Returns:
    
    Result of the GetScalingConfigurationRecommendation operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - getSearchSuggestions
```
GetSearchSuggestionsResult getSearchSuggestions(GetSearchSuggestionsRequest getSearchSuggestionsRequest)
```
    An auto-complete API for the search functionality in the SageMaker console. It returns suggestions of possible matches for the property name to use in Search queries. Provides suggestions for HyperParameters, Tags, and Metrics.
    
    Parameters:
    
    getSearchSuggestionsRequest -
    
    Returns:
    
    Result of the GetSearchSuggestions operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - importHubContent
```
ImportHubContentResult importHubContent(ImportHubContentRequest importHubContentRequest)
```
    Import hub content.
    
    Parameters:
    
    importHubContentRequest -
    
    Returns:
    
    Result of the ImportHubContent operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - listActions
```
ListActionsResult listActions(ListActionsRequest listActionsRequest)
```
    Lists the actions in your account and their properties.
    
    Parameters:
    
    listActionsRequest -
    
    Returns:
    
    Result of the ListActions operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - listAlgorithms
```
ListAlgorithmsResult listAlgorithms(ListAlgorithmsRequest listAlgorithmsRequest)
```
    Lists the machine learning algorithms that have been created.
    
    Parameters:
    
    listAlgorithmsRequest -
    
    Returns:
    
    Result of the ListAlgorithms operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listAliases
```
ListAliasesResult listAliases(ListAliasesRequest listAliasesRequest)
```
    Lists the aliases of a specified image or image version.
    
    Parameters:
    
    listAliasesRequest -
    
    Returns:
    
    Result of the ListAliases operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - listAppImageConfigs
```
ListAppImageConfigsResult listAppImageConfigs(ListAppImageConfigsRequest listAppImageConfigsRequest)
```
    Lists the AppImageConfigs in your account and their properties. The list can be filtered by creation time or modified time, and whether the AppImageConfig name contains a specified string.
    
    Parameters:
    
    listAppImageConfigsRequest -
    
    Returns:
    
    Result of the ListAppImageConfigs operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listApps
```
ListAppsResult listApps(ListAppsRequest listAppsRequest)
```
    Lists apps.
    
    Parameters:
    
    listAppsRequest -
    
    Returns:
    
    Result of the ListApps operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listArtifacts
```
ListArtifactsResult listArtifacts(ListArtifactsRequest listArtifactsRequest)
```
    Lists the artifacts in your account and their properties.
    
    Parameters:
    
    listArtifactsRequest -
    
    Returns:
    
    Result of the ListArtifacts operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - listAssociations
```
ListAssociationsResult listAssociations(ListAssociationsRequest listAssociationsRequest)
```
    Lists the associations in your account and their properties.
    
    Parameters:
    
    listAssociationsRequest -
    
    Returns:
    
    Result of the ListAssociations operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - listAutoMLJobs
```
ListAutoMLJobsResult listAutoMLJobs(ListAutoMLJobsRequest listAutoMLJobsRequest)
```
    Request a list of jobs.
    
    Parameters:
    
    listAutoMLJobsRequest -
    
    Returns:
    
    Result of the ListAutoMLJobs operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listCandidatesForAutoMLJob
```
ListCandidatesForAutoMLJobResult listCandidatesForAutoMLJob(ListCandidatesForAutoMLJobRequest listCandidatesForAutoMLJobRequest)
```
    List the candidates created for the job.
    
    Parameters:
    
    listCandidatesForAutoMLJobRequest -
    
    Returns:
    
    Result of the ListCandidatesForAutoMLJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - listClusterNodes
```
ListClusterNodesResult listClusterNodes(ListClusterNodesRequest listClusterNodesRequest)
```
    Retrieves the list of instances (also called nodes interchangeably) in a SageMaker HyperPod cluster.
    
    Parameters:
    
    listClusterNodesRequest -
    
    Returns:
    
    Result of the ListClusterNodes operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - listClusters
```
ListClustersResult listClusters(ListClustersRequest listClustersRequest)
```
    Retrieves the list of SageMaker HyperPod clusters.
    
    Parameters:
    
    listClustersRequest -
    
    Returns:
    
    Result of the ListClusters operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listCodeRepositories
```
ListCodeRepositoriesResult listCodeRepositories(ListCodeRepositoriesRequest listCodeRepositoriesRequest)
```
    Gets a list of the Git repositories in your account.
    
    Parameters:
    
    listCodeRepositoriesRequest -
    
    Returns:
    
    Result of the ListCodeRepositories operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listCompilationJobs
```
ListCompilationJobsResult listCompilationJobs(ListCompilationJobsRequest listCompilationJobsRequest)
```
    Lists model compilation jobs that satisfy various filters.
    
    To create a model compilation job, use CreateCompilationJob. To get information about a particular model compilation job you have created, use DescribeCompilationJob.
    
    Parameters:
    
    listCompilationJobsRequest -
    
    Returns:
    
    Result of the ListCompilationJobs operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listContexts
```
ListContextsResult listContexts(ListContextsRequest listContextsRequest)
```
    Lists the contexts in your account and their properties.
    
    Parameters:
    
    listContextsRequest -
    
    Returns:
    
    Result of the ListContexts operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - listDataQualityJobDefinitions
```
ListDataQualityJobDefinitionsResult listDataQualityJobDefinitions(ListDataQualityJobDefinitionsRequest listDataQualityJobDefinitionsRequest)
```
    Lists the data quality job definitions in your account.
    
    Parameters:
    
    listDataQualityJobDefinitionsRequest -
    
    Returns:
    
    Result of the ListDataQualityJobDefinitions operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listDeviceFleets
```
ListDeviceFleetsResult listDeviceFleets(ListDeviceFleetsRequest listDeviceFleetsRequest)
```
    Returns a list of devices in the fleet.
    
    Parameters:
    
    listDeviceFleetsRequest -
    
    Returns:
    
    Result of the ListDeviceFleets operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listDevices
```
ListDevicesResult listDevices(ListDevicesRequest listDevicesRequest)
```
    A list of devices.
    
    Parameters:
    
    listDevicesRequest -
    
    Returns:
    
    Result of the ListDevices operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listDomains
```
ListDomainsResult listDomains(ListDomainsRequest listDomainsRequest)
```
    Lists the domains.
    
    Parameters:
    
    listDomainsRequest -
    
    Returns:
    
    Result of the ListDomains operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listEdgeDeploymentPlans
```
ListEdgeDeploymentPlansResult listEdgeDeploymentPlans(ListEdgeDeploymentPlansRequest listEdgeDeploymentPlansRequest)
```
    Lists all edge deployment plans.
    
    Parameters:
    
    listEdgeDeploymentPlansRequest -
    
    Returns:
    
    Result of the ListEdgeDeploymentPlans operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listEdgePackagingJobs
```
ListEdgePackagingJobsResult listEdgePackagingJobs(ListEdgePackagingJobsRequest listEdgePackagingJobsRequest)
```
    Returns a list of edge packaging jobs.
    
    Parameters:
    
    listEdgePackagingJobsRequest -
    
    Returns:
    
    Result of the ListEdgePackagingJobs operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listEndpointConfigs
```
ListEndpointConfigsResult listEndpointConfigs(ListEndpointConfigsRequest listEndpointConfigsRequest)
```
    Lists endpoint configurations.
    
    Parameters:
    
    listEndpointConfigsRequest -
    
    Returns:
    
    Result of the ListEndpointConfigs operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listEndpoints
```
ListEndpointsResult listEndpoints(ListEndpointsRequest listEndpointsRequest)
```
    Lists endpoints.
    
    Parameters:
    
    listEndpointsRequest -
    
    Returns:
    
    Result of the ListEndpoints operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listExperiments
```
ListExperimentsResult listExperiments(ListExperimentsRequest listExperimentsRequest)
```
    Lists all the experiments in your account. The list can be filtered to show only experiments that were created in a specific time range. The list can be sorted by experiment name or creation time.
    
    Parameters:
    
    listExperimentsRequest -
    
    Returns:
    
    Result of the ListExperiments operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listFeatureGroups
```
ListFeatureGroupsResult listFeatureGroups(ListFeatureGroupsRequest listFeatureGroupsRequest)
```
    List FeatureGroups based on given filter and order.
    
    Parameters:
    
    listFeatureGroupsRequest -
    
    Returns:
    
    Result of the ListFeatureGroups operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listFlowDefinitions
```
ListFlowDefinitionsResult listFlowDefinitions(ListFlowDefinitionsRequest listFlowDefinitionsRequest)
```
    Returns information about the flow definitions in your account.
    
    Parameters:
    
    listFlowDefinitionsRequest -
    
    Returns:
    
    Result of the ListFlowDefinitions operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listHubContentVersions
```
ListHubContentVersionsResult listHubContentVersions(ListHubContentVersionsRequest listHubContentVersionsRequest)
```
    List hub content versions.
    
    Parameters:
    
    listHubContentVersionsRequest -
    
    Returns:
    
    Result of the ListHubContentVersions operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - listHubContents
```
ListHubContentsResult listHubContents(ListHubContentsRequest listHubContentsRequest)
```
    List the contents of a hub.
    
    Parameters:
    
    listHubContentsRequest -
    
    Returns:
    
    Result of the ListHubContents operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - listHubs
```
ListHubsResult listHubs(ListHubsRequest listHubsRequest)
```
    List all existing hubs.
    
    Parameters:
    
    listHubsRequest -
    
    Returns:
    
    Result of the ListHubs operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listHumanTaskUis
```
ListHumanTaskUisResult listHumanTaskUis(ListHumanTaskUisRequest listHumanTaskUisRequest)
```
    Returns information about the human task user interfaces in your account.
    
    Parameters:
    
    listHumanTaskUisRequest -
    
    Returns:
    
    Result of the ListHumanTaskUis operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listHyperParameterTuningJobs
```
ListHyperParameterTuningJobsResult listHyperParameterTuningJobs(ListHyperParameterTuningJobsRequest listHyperParameterTuningJobsRequest)
```
    Gets a list of HyperParameterTuningJobSummary objects that describe the hyperparameter tuning jobs launched in your account.
    
    Parameters:
    
    listHyperParameterTuningJobsRequest -
    
    Returns:
    
    Result of the ListHyperParameterTuningJobs operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listImageVersions
```
ListImageVersionsResult listImageVersions(ListImageVersionsRequest listImageVersionsRequest)
```
    Lists the versions of a specified image and their properties. The list can be filtered by creation time or modified time.
    
    Parameters:
    
    listImageVersionsRequest -
    
    Returns:
    
    Result of the ListImageVersions operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - listImages
```
ListImagesResult listImages(ListImagesRequest listImagesRequest)
```
    Lists the images in your account and their properties. The list can be filtered by creation time or modified time, and whether the image name contains a specified string.
    
    Parameters:
    
    listImagesRequest -
    
    Returns:
    
    Result of the ListImages operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listInferenceComponents
```
ListInferenceComponentsResult listInferenceComponents(ListInferenceComponentsRequest listInferenceComponentsRequest)
```
    Lists the inference components in your account and their properties.
    
    Parameters:
    
    listInferenceComponentsRequest -
    
    Returns:
    
    Result of the ListInferenceComponents operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listInferenceExperiments
```
ListInferenceExperimentsResult listInferenceExperiments(ListInferenceExperimentsRequest listInferenceExperimentsRequest)
```
    Returns the list of all inference experiments.
    
    Parameters:
    
    listInferenceExperimentsRequest -
    
    Returns:
    
    Result of the ListInferenceExperiments operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listInferenceRecommendationsJobSteps
```
ListInferenceRecommendationsJobStepsResult listInferenceRecommendationsJobSteps(ListInferenceRecommendationsJobStepsRequest listInferenceRecommendationsJobStepsRequest)
```
    Returns a list of the subtasks for an Inference Recommender job.
    
    The supported subtasks are benchmarks, which evaluate the performance of your model on different instance types.
    
    Parameters:
    
    listInferenceRecommendationsJobStepsRequest -
    
    Returns:
    
    Result of the ListInferenceRecommendationsJobSteps operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - listInferenceRecommendationsJobs
```
ListInferenceRecommendationsJobsResult listInferenceRecommendationsJobs(ListInferenceRecommendationsJobsRequest listInferenceRecommendationsJobsRequest)
```
    Lists recommendation jobs that satisfy various filters.
    
    Parameters:
    
    listInferenceRecommendationsJobsRequest -
    
    Returns:
    
    Result of the ListInferenceRecommendationsJobs operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listLabelingJobs
```
ListLabelingJobsResult listLabelingJobs(ListLabelingJobsRequest listLabelingJobsRequest)
```
    Gets a list of labeling jobs.
    
    Parameters:
    
    listLabelingJobsRequest -
    
    Returns:
    
    Result of the ListLabelingJobs operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listLabelingJobsForWorkteam
```
ListLabelingJobsForWorkteamResult listLabelingJobsForWorkteam(ListLabelingJobsForWorkteamRequest listLabelingJobsForWorkteamRequest)
```
    Gets a list of labeling jobs assigned to a specified work team.
    
    Parameters:
    
    listLabelingJobsForWorkteamRequest -
    
    Returns:
    
    Result of the ListLabelingJobsForWorkteam operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - listLineageGroups
```
ListLineageGroupsResult listLineageGroups(ListLineageGroupsRequest listLineageGroupsRequest)
```
    A list of lineage groups shared with your Amazon Web Services account. For more information, see Cross-Account Lineage Tracking in the Amazon SageMaker Developer Guide.
    
    Parameters:
    
    listLineageGroupsRequest -
    
    Returns:
    
    Result of the ListLineageGroups operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listMlflowTrackingServers
```
ListMlflowTrackingServersResult listMlflowTrackingServers(ListMlflowTrackingServersRequest listMlflowTrackingServersRequest)
```
    Lists all MLflow Tracking Servers.
    
    Parameters:
    
    listMlflowTrackingServersRequest -
    
    Returns:
    
    Result of the ListMlflowTrackingServers operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listModelBiasJobDefinitions
```
ListModelBiasJobDefinitionsResult listModelBiasJobDefinitions(ListModelBiasJobDefinitionsRequest listModelBiasJobDefinitionsRequest)
```
    Lists model bias jobs definitions that satisfy various filters.
    
    Parameters:
    
    listModelBiasJobDefinitionsRequest -
    
    Returns:
    
    Result of the ListModelBiasJobDefinitions operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listModelCardExportJobs
```
ListModelCardExportJobsResult listModelCardExportJobs(ListModelCardExportJobsRequest listModelCardExportJobsRequest)
```
    List the export jobs for the Amazon SageMaker Model Card.
    
    Parameters:
    
    listModelCardExportJobsRequest -
    
    Returns:
    
    Result of the ListModelCardExportJobs operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listModelCardVersions
```
ListModelCardVersionsResult listModelCardVersions(ListModelCardVersionsRequest listModelCardVersionsRequest)
```
    List existing versions of an Amazon SageMaker Model Card.
    
    Parameters:
    
    listModelCardVersionsRequest -
    
    Returns:
    
    Result of the ListModelCardVersions operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - listModelCards
```
ListModelCardsResult listModelCards(ListModelCardsRequest listModelCardsRequest)
```
    List existing model cards.
    
    Parameters:
    
    listModelCardsRequest -
    
    Returns:
    
    Result of the ListModelCards operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listModelExplainabilityJobDefinitions
```
ListModelExplainabilityJobDefinitionsResult listModelExplainabilityJobDefinitions(ListModelExplainabilityJobDefinitionsRequest listModelExplainabilityJobDefinitionsRequest)
```
    Lists model explainability job definitions that satisfy various filters.
    
    Parameters:
    
    listModelExplainabilityJobDefinitionsRequest -
    
    Returns:
    
    Result of the ListModelExplainabilityJobDefinitions operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listModelMetadata
```
ListModelMetadataResult listModelMetadata(ListModelMetadataRequest listModelMetadataRequest)
```
    Lists the domain, framework, task, and model name of standard machine learning models found in common model zoos.
    
    Parameters:
    
    listModelMetadataRequest -
    
    Returns:
    
    Result of the ListModelMetadata operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listModelPackageGroups
```
ListModelPackageGroupsResult listModelPackageGroups(ListModelPackageGroupsRequest listModelPackageGroupsRequest)
```
    Gets a list of the model groups in your Amazon Web Services account.
    
    Parameters:
    
    listModelPackageGroupsRequest -
    
    Returns:
    
    Result of the ListModelPackageGroups operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listModelPackages
```
ListModelPackagesResult listModelPackages(ListModelPackagesRequest listModelPackagesRequest)
```
    Lists the model packages that have been created.
    
    Parameters:
    
    listModelPackagesRequest -
    
    Returns:
    
    Result of the ListModelPackages operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listModelQualityJobDefinitions
```
ListModelQualityJobDefinitionsResult listModelQualityJobDefinitions(ListModelQualityJobDefinitionsRequest listModelQualityJobDefinitionsRequest)
```
    Gets a list of model quality monitoring job definitions in your account.
    
    Parameters:
    
    listModelQualityJobDefinitionsRequest -
    
    Returns:
    
    Result of the ListModelQualityJobDefinitions operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listModels
```
ListModelsResult listModels(ListModelsRequest listModelsRequest)
```
    Lists models created with the CreateModel API.
    
    Parameters:
    
    listModelsRequest -
    
    Returns:
    
    Result of the ListModels operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listMonitoringAlertHistory
```
ListMonitoringAlertHistoryResult listMonitoringAlertHistory(ListMonitoringAlertHistoryRequest listMonitoringAlertHistoryRequest)
```
    Gets a list of past alerts in a model monitoring schedule.
    
    Parameters:
    
    listMonitoringAlertHistoryRequest -
    
    Returns:
    
    Result of the ListMonitoringAlertHistory operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - listMonitoringAlerts
```
ListMonitoringAlertsResult listMonitoringAlerts(ListMonitoringAlertsRequest listMonitoringAlertsRequest)
```
    Gets the alerts for a single monitoring schedule.
    
    Parameters:
    
    listMonitoringAlertsRequest -
    
    Returns:
    
    Result of the ListMonitoringAlerts operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - listMonitoringExecutions
```
ListMonitoringExecutionsResult listMonitoringExecutions(ListMonitoringExecutionsRequest listMonitoringExecutionsRequest)
```
    Returns list of all monitoring job executions.
    
    Parameters:
    
    listMonitoringExecutionsRequest -
    
    Returns:
    
    Result of the ListMonitoringExecutions operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listMonitoringSchedules
```
ListMonitoringSchedulesResult listMonitoringSchedules(ListMonitoringSchedulesRequest listMonitoringSchedulesRequest)
```
    Returns list of all monitoring schedules.
    
    Parameters:
    
    listMonitoringSchedulesRequest -
    
    Returns:
    
    Result of the ListMonitoringSchedules operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listNotebookInstanceLifecycleConfigs
```
ListNotebookInstanceLifecycleConfigsResult listNotebookInstanceLifecycleConfigs(ListNotebookInstanceLifecycleConfigsRequest listNotebookInstanceLifecycleConfigsRequest)
```
    Lists notebook instance lifestyle configurations created with the CreateNotebookInstanceLifecycleConfig API.
    
    Parameters:
    
    listNotebookInstanceLifecycleConfigsRequest -
    
    Returns:
    
    Result of the ListNotebookInstanceLifecycleConfigs operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listNotebookInstances
```
ListNotebookInstancesResult listNotebookInstances(ListNotebookInstancesRequest listNotebookInstancesRequest)
```
    Returns a list of the SageMaker notebook instances in the requester's account in an Amazon Web Services Region.
    
    Parameters:
    
    listNotebookInstancesRequest -
    
    Returns:
    
    Result of the ListNotebookInstances operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listOptimizationJobs
```
ListOptimizationJobsResult listOptimizationJobs(ListOptimizationJobsRequest listOptimizationJobsRequest)
```
    Lists the optimization jobs in your account and their properties.
    
    Parameters:
    
    listOptimizationJobsRequest -
    
    Returns:
    
    Result of the ListOptimizationJobs operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listPipelineExecutionSteps
```
ListPipelineExecutionStepsResult listPipelineExecutionSteps(ListPipelineExecutionStepsRequest listPipelineExecutionStepsRequest)
```
    Gets a list of PipeLineExecutionStep objects.
    
    Parameters:
    
    listPipelineExecutionStepsRequest -
    
    Returns:
    
    Result of the ListPipelineExecutionSteps operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - listPipelineExecutions
```
ListPipelineExecutionsResult listPipelineExecutions(ListPipelineExecutionsRequest listPipelineExecutionsRequest)
```
    Gets a list of the pipeline executions.
    
    Parameters:
    
    listPipelineExecutionsRequest -
    
    Returns:
    
    Result of the ListPipelineExecutions operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - listPipelineParametersForExecution
```
ListPipelineParametersForExecutionResult listPipelineParametersForExecution(ListPipelineParametersForExecutionRequest listPipelineParametersForExecutionRequest)
```
    Gets a list of parameters for a pipeline execution.
    
    Parameters:
    
    listPipelineParametersForExecutionRequest -
    
    Returns:
    
    Result of the ListPipelineParametersForExecution operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - listPipelines
```
ListPipelinesResult listPipelines(ListPipelinesRequest listPipelinesRequest)
```
    Gets a list of pipelines.
    
    Parameters:
    
    listPipelinesRequest -
    
    Returns:
    
    Result of the ListPipelines operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listProcessingJobs
```
ListProcessingJobsResult listProcessingJobs(ListProcessingJobsRequest listProcessingJobsRequest)
```
    Lists processing jobs that satisfy various filters.
    
    Parameters:
    
    listProcessingJobsRequest -
    
    Returns:
    
    Result of the ListProcessingJobs operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listProjects
```
ListProjectsResult listProjects(ListProjectsRequest listProjectsRequest)
```
    Gets a list of the projects in an Amazon Web Services account.
    
    Parameters:
    
    listProjectsRequest -
    
    Returns:
    
    Result of the ListProjects operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listResourceCatalogs
```
ListResourceCatalogsResult listResourceCatalogs(ListResourceCatalogsRequest listResourceCatalogsRequest)
```
    Lists Amazon SageMaker Catalogs based on given filters and orders. The maximum number of ResourceCatalogs viewable is 1000.
    
    Parameters:
    
    listResourceCatalogsRequest -
    
    Returns:
    
    Result of the ListResourceCatalogs operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listSpaces
```
ListSpacesResult listSpaces(ListSpacesRequest listSpacesRequest)
```
    Lists spaces.
    
    Parameters:
    
    listSpacesRequest -
    
    Returns:
    
    Result of the ListSpaces operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listStageDevices
```
ListStageDevicesResult listStageDevices(ListStageDevicesRequest listStageDevicesRequest)
```
    Lists devices allocated to the stage, containing detailed device information and deployment status.
    
    Parameters:
    
    listStageDevicesRequest -
    
    Returns:
    
    Result of the ListStageDevices operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listStudioLifecycleConfigs
```
ListStudioLifecycleConfigsResult listStudioLifecycleConfigs(ListStudioLifecycleConfigsRequest listStudioLifecycleConfigsRequest)
```
    Lists the Amazon SageMaker Studio Lifecycle Configurations in your Amazon Web Services Account.
    
    Parameters:
    
    listStudioLifecycleConfigsRequest -
    
    Returns:
    
    Result of the ListStudioLifecycleConfigs operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    See Also:
    
    AWS API Documentation
  - listSubscribedWorkteams
```
ListSubscribedWorkteamsResult listSubscribedWorkteams(ListSubscribedWorkteamsRequest listSubscribedWorkteamsRequest)
```
    Gets a list of the work teams that you are subscribed to in the Amazon Web Services Marketplace. The list may be empty if no work team satisfies the filter specified in the NameContains parameter.
    
    Parameters:
    
    listSubscribedWorkteamsRequest -
    
    Returns:
    
    Result of the ListSubscribedWorkteams operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listTags
```
ListTagsResult listTags(ListTagsRequest listTagsRequest)
```
    Returns the tags for the specified SageMaker resource.
    
    Parameters:
    
    listTagsRequest -
    
    Returns:
    
    Result of the ListTags operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listTrainingJobs
```
ListTrainingJobsResult listTrainingJobs(ListTrainingJobsRequest listTrainingJobsRequest)
```
    Lists training jobs.
    
    When StatusEquals and MaxResults are set at the same time, the MaxResults number of training jobs are first retrieved ignoring the StatusEquals parameter and then they are filtered by the StatusEquals parameter, which is returned as a response.
    
    For example, if ListTrainingJobs is invoked with the following parameters:
    
    { ... MaxResults: 100, StatusEquals: InProgress ... }
    
    First, 100 trainings jobs with any status, including those other than InProgress, are selected (sorted according to the creation time, from the most current to the oldest). Next, those with a status of InProgress are returned.
    
    You can quickly test the API using the following Amazon Web Services CLI code.
    
    aws sagemaker list-training-jobs --max-results 100 --status-equals InProgress
    
    Parameters:
    
    listTrainingJobsRequest -
    
    Returns:
    
    Result of the ListTrainingJobs operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listTrainingJobsForHyperParameterTuningJob
```
ListTrainingJobsForHyperParameterTuningJobResult listTrainingJobsForHyperParameterTuningJob(ListTrainingJobsForHyperParameterTuningJobRequest listTrainingJobsForHyperParameterTuningJobRequest)
```
    Gets a list of TrainingJobSummary objects that describe the training jobs that a hyperparameter tuning job launched.
    
    Parameters:
    
    listTrainingJobsForHyperParameterTuningJobRequest -
    
    Returns:
    
    Result of the ListTrainingJobsForHyperParameterTuningJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - listTransformJobs
```
ListTransformJobsResult listTransformJobs(ListTransformJobsRequest listTransformJobsRequest)
```
    Lists transform jobs.
    
    Parameters:
    
    listTransformJobsRequest -
    
    Returns:
    
    Result of the ListTransformJobs operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listTrialComponents
```
ListTrialComponentsResult listTrialComponents(ListTrialComponentsRequest listTrialComponentsRequest)
```
    Lists the trial components in your account. You can sort the list by trial component name or creation time. You can filter the list to show only components that were created in a specific time range. You can also filter on one of the following:
    - ExperimentName
    - SourceArn
    - TrialName
    Parameters:
    
    listTrialComponentsRequest -
    
    Returns:
    
    Result of the ListTrialComponents operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - listTrials
```
ListTrialsResult listTrials(ListTrialsRequest listTrialsRequest)
```
    Lists the trials in your account. Specify an experiment name to limit the list to the trials that are part of that experiment. Specify a trial component name to limit the list to the trials that associated with that trial component. The list can be filtered to show only trials that were created in a specific time range. The list can be sorted by trial name or creation time.
    
    Parameters:
    
    listTrialsRequest -
    
    Returns:
    
    Result of the ListTrials operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - listUserProfiles
```
ListUserProfilesResult listUserProfiles(ListUserProfilesRequest listUserProfilesRequest)
```
    Lists user profiles.
    
    Parameters:
    
    listUserProfilesRequest -
    
    Returns:
    
    Result of the ListUserProfiles operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listWorkforces
```
ListWorkforcesResult listWorkforces(ListWorkforcesRequest listWorkforcesRequest)
```
    Use this operation to list all private and vendor workforces in an Amazon Web Services Region. Note that you can only have one private workforce per Amazon Web Services Region.
    
    Parameters:
    
    listWorkforcesRequest -
    
    Returns:
    
    Result of the ListWorkforces operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listWorkteams
```
ListWorkteamsResult listWorkteams(ListWorkteamsRequest listWorkteamsRequest)
```
    Gets a list of private work teams that you have defined in a region. The list may be empty if no work team satisfies the filter specified in the NameContains parameter.
    
    Parameters:
    
    listWorkteamsRequest -
    
    Returns:
    
    Result of the ListWorkteams operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - putModelPackageGroupPolicy
```
PutModelPackageGroupPolicyResult putModelPackageGroupPolicy(PutModelPackageGroupPolicyRequest putModelPackageGroupPolicyRequest)
```
    Adds a resouce policy to control access to a model group. For information about resoure policies, see Identity-based policies and resource-based policies in the Amazon Web Services Identity and Access Management User Guide..
    
    Parameters:
    
    putModelPackageGroupPolicyRequest -
    
    Returns:
    
    Result of the PutModelPackageGroupPolicy operation returned by the service.
    
    Throws:
    
    ConflictException - There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.
    
    See Also:
    
    AWS API Documentation
  - queryLineage
```
QueryLineageResult queryLineage(QueryLineageRequest queryLineageRequest)
```
    Use this action to inspect your lineage and discover relationships between entities. For more information, see Querying Lineage Entities in the Amazon SageMaker Developer Guide.
    
    Parameters:
    
    queryLineageRequest -
    
    Returns:
    
    Result of the QueryLineage operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - registerDevices
```
RegisterDevicesResult registerDevices(RegisterDevicesRequest registerDevicesRequest)
```
    Register devices.
    
    Parameters:
    
    registerDevicesRequest -
    
    Returns:
    
    Result of the RegisterDevices operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - renderUiTemplate
```
RenderUiTemplateResult renderUiTemplate(RenderUiTemplateRequest renderUiTemplateRequest)
```
    Renders the UI template so that you can preview the worker's experience.
    
    Parameters:
    
    renderUiTemplateRequest -
    
    Returns:
    
    Result of the RenderUiTemplate operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - retryPipelineExecution
```
RetryPipelineExecutionResult retryPipelineExecution(RetryPipelineExecutionRequest retryPipelineExecutionRequest)
```
    Retry the execution of the pipeline.
    
    Parameters:
    
    retryPipelineExecutionRequest -
    
    Returns:
    
    Result of the RetryPipelineExecution operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    ConflictException - There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.
    
    See Also:
    
    AWS API Documentation
  - search
```
SearchResult search(SearchRequest searchRequest)
```
    Finds SageMaker resources that match a search query. Matching resources are returned as a list of SearchRecord objects in the response. You can sort the search results by any resource property in a ascending or descending order.
    
    You can query against the following value types: numeric, text, Boolean, and timestamp.
    
    The Search API may provide access to otherwise restricted data. See Amazon SageMaker API Permissions: Actions, Permissions, and Resources Reference for more information.
    
    Parameters:
    
    searchRequest -
    
    Returns:
    
    Result of the Search operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - sendPipelineExecutionStepFailure
```
SendPipelineExecutionStepFailureResult sendPipelineExecutionStepFailure(SendPipelineExecutionStepFailureRequest sendPipelineExecutionStepFailureRequest)
```
    Notifies the pipeline that the execution of a callback step failed, along with a message describing why. When a callback step is run, the pipeline generates a callback token and includes the token in a message sent to Amazon Simple Queue Service (Amazon SQS).
    
    Parameters:
    
    sendPipelineExecutionStepFailureRequest -
    
    Returns:
    
    Result of the SendPipelineExecutionStepFailure operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    ConflictException - There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.
    
    See Also:
    
    AWS API Documentation
  - sendPipelineExecutionStepSuccess
```
SendPipelineExecutionStepSuccessResult sendPipelineExecutionStepSuccess(SendPipelineExecutionStepSuccessRequest sendPipelineExecutionStepSuccessRequest)
```
    Notifies the pipeline that the execution of a callback step succeeded and provides a list of the step's output parameters. When a callback step is run, the pipeline generates a callback token and includes the token in a message sent to Amazon Simple Queue Service (Amazon SQS).
    
    Parameters:
    
    sendPipelineExecutionStepSuccessRequest -
    
    Returns:
    
    Result of the SendPipelineExecutionStepSuccess operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    ConflictException - There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.
    
    See Also:
    
    AWS API Documentation
  - startEdgeDeploymentStage
```
StartEdgeDeploymentStageResult startEdgeDeploymentStage(StartEdgeDeploymentStageRequest startEdgeDeploymentStageRequest)
```
    Starts a stage in an edge deployment plan.
    
    Parameters:
    
    startEdgeDeploymentStageRequest -
    
    Returns:
    
    Result of the StartEdgeDeploymentStage operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - startInferenceExperiment
```
StartInferenceExperimentResult startInferenceExperiment(StartInferenceExperimentRequest startInferenceExperimentRequest)
```
    Starts an inference experiment.
    
    Parameters:
    
    startInferenceExperimentRequest -
    
    Returns:
    
    Result of the StartInferenceExperiment operation returned by the service.
    
    Throws:
    
    ConflictException - There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - startMlflowTrackingServer
```
StartMlflowTrackingServerResult startMlflowTrackingServer(StartMlflowTrackingServerRequest startMlflowTrackingServerRequest)
```
    Programmatically start an MLflow Tracking Server.
    
    Parameters:
    
    startMlflowTrackingServerRequest -
    
    Returns:
    
    Result of the StartMlflowTrackingServer operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    ConflictException - There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.
    
    See Also:
    
    AWS API Documentation
  - startMonitoringSchedule
```
StartMonitoringScheduleResult startMonitoringSchedule(StartMonitoringScheduleRequest startMonitoringScheduleRequest)
```
    Starts a previously stopped monitoring schedule.
    
    By default, when you successfully create a new schedule, the status of a monitoring schedule is scheduled.
    
    Parameters:
    
    startMonitoringScheduleRequest -
    
    Returns:
    
    Result of the StartMonitoringSchedule operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - startNotebookInstance
```
StartNotebookInstanceResult startNotebookInstance(StartNotebookInstanceRequest startNotebookInstanceRequest)
```
    Launches an ML compute instance with the latest version of the libraries and attaches your ML storage volume. After configuring the notebook instance, SageMaker sets the notebook instance status to InService. A notebook instance's status must be InService before you can connect to your Jupyter notebook.
    
    Parameters:
    
    startNotebookInstanceRequest -
    
    Returns:
    
    Result of the StartNotebookInstance operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - startPipelineExecution
```
StartPipelineExecutionResult startPipelineExecution(StartPipelineExecutionRequest startPipelineExecutionRequest)
```
    Starts a pipeline execution.
    
    Parameters:
    
    startPipelineExecutionRequest -
    
    Returns:
    
    Result of the StartPipelineExecution operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    ConflictException - There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.
    
    See Also:
    
    AWS API Documentation
  - stopAutoMLJob
```
StopAutoMLJobResult stopAutoMLJob(StopAutoMLJobRequest stopAutoMLJobRequest)
```
    A method for forcing a running job to shut down.
    
    Parameters:
    
    stopAutoMLJobRequest -
    
    Returns:
    
    Result of the StopAutoMLJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - stopCompilationJob
```
StopCompilationJobResult stopCompilationJob(StopCompilationJobRequest stopCompilationJobRequest)
```
    Stops a model compilation job.
    
    To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal. This gracefully shuts the job down. If the job hasn't stopped, it sends the SIGKILL signal.
    
    When it receives a StopCompilationJob request, Amazon SageMaker changes the CompilationJobStatus of the job to Stopping. After Amazon SageMaker stops the job, it sets the CompilationJobStatus to Stopped.
    
    Parameters:
    
    stopCompilationJobRequest -
    
    Returns:
    
    Result of the StopCompilationJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - stopEdgeDeploymentStage
```
StopEdgeDeploymentStageResult stopEdgeDeploymentStage(StopEdgeDeploymentStageRequest stopEdgeDeploymentStageRequest)
```
    Stops a stage in an edge deployment plan.
    
    Parameters:
    
    stopEdgeDeploymentStageRequest -
    
    Returns:
    
    Result of the StopEdgeDeploymentStage operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - stopEdgePackagingJob
```
StopEdgePackagingJobResult stopEdgePackagingJob(StopEdgePackagingJobRequest stopEdgePackagingJobRequest)
```
    Request to stop an edge packaging job.
    
    Parameters:
    
    stopEdgePackagingJobRequest -
    
    Returns:
    
    Result of the StopEdgePackagingJob operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - stopHyperParameterTuningJob
```
StopHyperParameterTuningJobResult stopHyperParameterTuningJob(StopHyperParameterTuningJobRequest stopHyperParameterTuningJobRequest)
```
    Stops a running hyperparameter tuning job and all running training jobs that the tuning job launched.
    
    All model artifacts output from the training jobs are stored in Amazon Simple Storage Service (Amazon S3). All data that the training jobs write to Amazon CloudWatch Logs are still available in CloudWatch. After the tuning job moves to the Stopped state, it releases all reserved resources for the tuning job.
    
    Parameters:
    
    stopHyperParameterTuningJobRequest -
    
    Returns:
    
    Result of the StopHyperParameterTuningJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - stopInferenceExperiment
```
StopInferenceExperimentResult stopInferenceExperiment(StopInferenceExperimentRequest stopInferenceExperimentRequest)
```
    Stops an inference experiment.
    
    Parameters:
    
    stopInferenceExperimentRequest -
    
    Returns:
    
    Result of the StopInferenceExperiment operation returned by the service.
    
    Throws:
    
    ConflictException - There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - stopInferenceRecommendationsJob
```
StopInferenceRecommendationsJobResult stopInferenceRecommendationsJob(StopInferenceRecommendationsJobRequest stopInferenceRecommendationsJobRequest)
```
    Stops an Inference Recommender job.
    
    Parameters:
    
    stopInferenceRecommendationsJobRequest -
    
    Returns:
    
    Result of the StopInferenceRecommendationsJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - stopLabelingJob
```
StopLabelingJobResult stopLabelingJob(StopLabelingJobRequest stopLabelingJobRequest)
```
    Stops a running labeling job. A job that is stopped cannot be restarted. Any results obtained before the job is stopped are placed in the Amazon S3 output bucket.
    
    Parameters:
    
    stopLabelingJobRequest -
    
    Returns:
    
    Result of the StopLabelingJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - stopMlflowTrackingServer
```
StopMlflowTrackingServerResult stopMlflowTrackingServer(StopMlflowTrackingServerRequest stopMlflowTrackingServerRequest)
```
    Programmatically stop an MLflow Tracking Server.
    
    Parameters:
    
    stopMlflowTrackingServerRequest -
    
    Returns:
    
    Result of the StopMlflowTrackingServer operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    ConflictException - There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.
    
    See Also:
    
    AWS API Documentation
  - stopMonitoringSchedule
```
StopMonitoringScheduleResult stopMonitoringSchedule(StopMonitoringScheduleRequest stopMonitoringScheduleRequest)
```
    Stops a previously started monitoring schedule.
    
    Parameters:
    
    stopMonitoringScheduleRequest -
    
    Returns:
    
    Result of the StopMonitoringSchedule operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - stopNotebookInstance
```
StopNotebookInstanceResult stopNotebookInstance(StopNotebookInstanceRequest stopNotebookInstanceRequest)
```
    Terminates the ML compute instance. Before terminating the instance, SageMaker disconnects the ML storage volume from it. SageMaker preserves the ML storage volume. SageMaker stops charging you for the ML compute instance when you call StopNotebookInstance.
    
    To access data on the ML storage volume for a notebook instance that has been terminated, call the StartNotebookInstance API. StartNotebookInstance launches another ML compute instance, configures it, and attaches the preserved ML storage volume so you can continue your work.
    
    Parameters:
    
    stopNotebookInstanceRequest -
    
    Returns:
    
    Result of the StopNotebookInstance operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - stopOptimizationJob
```
StopOptimizationJobResult stopOptimizationJob(StopOptimizationJobRequest stopOptimizationJobRequest)
```
    Ends a running inference optimization job.
    
    Parameters:
    
    stopOptimizationJobRequest -
    
    Returns:
    
    Result of the StopOptimizationJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - stopPipelineExecution
```
StopPipelineExecutionResult stopPipelineExecution(StopPipelineExecutionRequest stopPipelineExecutionRequest)
```
    Stops a pipeline execution.
    
    Callback Step
    
    A pipeline execution won't stop while a callback step is running. When you call StopPipelineExecution on a pipeline execution with a running callback step, SageMaker Pipelines sends an additional Amazon SQS message to the specified SQS queue. The body of the SQS message contains a "Status" field which is set to "Stopping".
    
    You should add logic to your Amazon SQS message consumer to take any needed action (for example, resource cleanup) upon receipt of the message followed by a call to SendPipelineExecutionStepSuccess or SendPipelineExecutionStepFailure.
    
    Only when SageMaker Pipelines receives one of these calls will it stop the pipeline execution.
    
    Lambda Step
    
    A pipeline execution can't be stopped while a lambda step is running because the Lambda function invoked by the lambda step can't be stopped. If you attempt to stop the execution while the Lambda function is running, the pipeline waits for the Lambda function to finish or until the timeout is hit, whichever occurs first, and then stops. If the Lambda function finishes, the pipeline execution status is Stopped. If the timeout is hit the pipeline execution status is Failed.
    
    Parameters:
    
    stopPipelineExecutionRequest -
    
    Returns:
    
    Result of the StopPipelineExecution operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    ConflictException - There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.
    
    See Also:
    
    AWS API Documentation
  - stopProcessingJob
```
StopProcessingJobResult stopProcessingJob(StopProcessingJobRequest stopProcessingJobRequest)
```
    Stops a processing job.
    
    Parameters:
    
    stopProcessingJobRequest -
    
    Returns:
    
    Result of the StopProcessingJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - stopTrainingJob
```
StopTrainingJobResult stopTrainingJob(StopTrainingJobRequest stopTrainingJobRequest)
```
    Stops a training job. To stop a job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms might use this 120-second window to save the model artifacts, so the results of the training is not lost.
    
    When it receives a StopTrainingJob request, SageMaker changes the status of the job to Stopping. After SageMaker stops the job, it sets the status to Stopped.
    
    Parameters:
    
    stopTrainingJobRequest -
    
    Returns:
    
    Result of the StopTrainingJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - stopTransformJob
```
StopTransformJobResult stopTransformJob(StopTransformJobRequest stopTransformJobRequest)
```
    Stops a batch transform job.
    
    When Amazon SageMaker receives a StopTransformJob request, the status of the job changes to Stopping. After Amazon SageMaker stops the job, the status is set to Stopped. When you stop a batch transform job before it is completed, Amazon SageMaker doesn't store the job's output in Amazon S3.
    
    Parameters:
    
    stopTransformJobRequest -
    
    Returns:
    
    Result of the StopTransformJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - updateAction
```
UpdateActionResult updateAction(UpdateActionRequest updateActionRequest)
```
    Updates an action.
    
    Parameters:
    
    updateActionRequest -
    
    Returns:
    
    Result of the UpdateAction operation returned by the service.
    
    Throws:
    
    ConflictException - There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - updateAppImageConfig
```
UpdateAppImageConfigResult updateAppImageConfig(UpdateAppImageConfigRequest updateAppImageConfigRequest)
```
    Updates the properties of an AppImageConfig.
    
    Parameters:
    
    updateAppImageConfigRequest -
    
    Returns:
    
    Result of the UpdateAppImageConfig operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - updateArtifact
```
UpdateArtifactResult updateArtifact(UpdateArtifactRequest updateArtifactRequest)
```
    Updates an artifact.
    
    Parameters:
    
    updateArtifactRequest -
    
    Returns:
    
    Result of the UpdateArtifact operation returned by the service.
    
    Throws:
    
    ConflictException - There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - updateCluster
```
UpdateClusterResult updateCluster(UpdateClusterRequest updateClusterRequest)
```
    Updates a SageMaker HyperPod cluster.
    
    Parameters:
    
    updateClusterRequest -
    
    Returns:
    
    Result of the UpdateCluster operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    ResourceNotFoundException - Resource being access is not found.
    
    ConflictException - There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.
    
    See Also:
    
    AWS API Documentation
  - updateClusterSoftware
```
UpdateClusterSoftwareResult updateClusterSoftware(UpdateClusterSoftwareRequest updateClusterSoftwareRequest)
```
    Updates the platform software of a SageMaker HyperPod cluster for security patching. To learn how to use this API, see Update the SageMaker HyperPod platform software of a cluster.
    
    Parameters:
    
    updateClusterSoftwareRequest -
    
    Returns:
    
    Result of the UpdateClusterSoftware operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    ConflictException - There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.
    
    See Also:
    
    AWS API Documentation
  - updateCodeRepository
```
UpdateCodeRepositoryResult updateCodeRepository(UpdateCodeRepositoryRequest updateCodeRepositoryRequest)
```
    Updates the specified Git repository with the specified values.
    
    Parameters:
    
    updateCodeRepositoryRequest -
    
    Returns:
    
    Result of the UpdateCodeRepository operation returned by the service.
    
    Throws:
    
    ConflictException - There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.
    
    See Also:
    
    AWS API Documentation
  - updateContext
```
UpdateContextResult updateContext(UpdateContextRequest updateContextRequest)
```
    Updates a context.
    
    Parameters:
    
    updateContextRequest -
    
    Returns:
    
    Result of the UpdateContext operation returned by the service.
    
    Throws:
    
    ConflictException - There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - updateDeviceFleet
```
UpdateDeviceFleetResult updateDeviceFleet(UpdateDeviceFleetRequest updateDeviceFleetRequest)
```
    Updates a fleet of devices.
    
    Parameters:
    
    updateDeviceFleetRequest -
    
    Returns:
    
    Result of the UpdateDeviceFleet operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    See Also:
    
    AWS API Documentation
  - updateDevices
```
UpdateDevicesResult updateDevices(UpdateDevicesRequest updateDevicesRequest)
```
    Updates one or more devices in a fleet.
    
    Parameters:
    
    updateDevicesRequest -
    
    Returns:
    
    Result of the UpdateDevices operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - updateDomain
```
UpdateDomainResult updateDomain(UpdateDomainRequest updateDomainRequest)
```
    Updates the default settings for new user profiles in the domain.
    
    Parameters:
    
    updateDomainRequest -
    
    Returns:
    
    Result of the UpdateDomain operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - updateEndpoint
```
UpdateEndpointResult updateEndpoint(UpdateEndpointRequest updateEndpointRequest)
```
    Deploys the EndpointConfig specified in the request to a new fleet of instances. SageMaker shifts endpoint traffic to the new instances with the updated endpoint configuration and then deletes the old instances using the previous EndpointConfig (there is no availability loss). For more information about how to control the update and traffic shifting process, see Update models in production.
    
    When SageMaker receives the request, it sets the endpoint status to Updating. After updating the endpoint, it sets the status to InService. To check the status of an endpoint, use the DescribeEndpoint API.
    
    You must not delete an EndpointConfig in use by an endpoint that is live or while the UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. To update an endpoint, you must create a new EndpointConfig.
    
    If you delete the EndpointConfig of an endpoint that is active or being created or updated you may lose visibility into the instance type the endpoint is using. The endpoint must be deleted in order to stop incurring charges.
    
    Parameters:
    
    updateEndpointRequest -
    
    Returns:
    
    Result of the UpdateEndpoint operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - updateEndpointWeightsAndCapacities
```
UpdateEndpointWeightsAndCapacitiesResult updateEndpointWeightsAndCapacities(UpdateEndpointWeightsAndCapacitiesRequest updateEndpointWeightsAndCapacitiesRequest)
```
    Updates variant weight of one or more variants associated with an existing endpoint, or capacity of one variant associated with an existing endpoint. When it receives the request, SageMaker sets the endpoint status to Updating. After updating the endpoint, it sets the status to InService. To check the status of an endpoint, use the DescribeEndpoint API.
    
    Parameters:
    
    updateEndpointWeightsAndCapacitiesRequest -
    
    Returns:
    
    Result of the UpdateEndpointWeightsAndCapacities operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - updateExperiment
```
UpdateExperimentResult updateExperiment(UpdateExperimentRequest updateExperimentRequest)
```
    Adds, updates, or removes the description of an experiment. Updates the display name of an experiment.
    
    Parameters:
    
    updateExperimentRequest -
    
    Returns:
    
    Result of the UpdateExperiment operation returned by the service.
    
    Throws:
    
    ConflictException - There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - updateFeatureGroup
```
UpdateFeatureGroupResult updateFeatureGroup(UpdateFeatureGroupRequest updateFeatureGroupRequest)
```
    Updates the feature group by either adding features or updating the online store configuration. Use one of the following request parameters at a time while using the UpdateFeatureGroup API.
    
    You can add features for your feature group using the FeatureAdditions request parameter. Features cannot be removed from a feature group.
    
    You can update the online store configuration by using the OnlineStoreConfig request parameter. If a TtlDuration is specified, the default TtlDuration applies for all records added to the feature group after the feature group is updated. If a record level TtlDuration exists from using the PutRecord API, the record level TtlDuration applies to that record instead of the default TtlDuration. To remove the default TtlDuration from an existing feature group, use the UpdateFeatureGroup API and set the TtlDuration Unit and Value to null.
    
    Parameters:
    
    updateFeatureGroupRequest -
    
    Returns:
    
    Result of the UpdateFeatureGroup operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - updateFeatureMetadata
```
UpdateFeatureMetadataResult updateFeatureMetadata(UpdateFeatureMetadataRequest updateFeatureMetadataRequest)
```
    Updates the description and parameters of the feature group.
    
    Parameters:
    
    updateFeatureMetadataRequest -
    
    Returns:
    
    Result of the UpdateFeatureMetadata operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - updateHub
```
UpdateHubResult updateHub(UpdateHubRequest updateHubRequest)
```
    Update a hub.
    
    Parameters:
    
    updateHubRequest -
    
    Returns:
    
    Result of the UpdateHub operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - updateImage
```
UpdateImageResult updateImage(UpdateImageRequest updateImageRequest)
```
    Updates the properties of a SageMaker image. To change the image's tags, use the AddTags and DeleteTags APIs.
    
    Parameters:
    
    updateImageRequest -
    
    Returns:
    
    Result of the UpdateImage operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - updateImageVersion
```
UpdateImageVersionResult updateImageVersion(UpdateImageVersionRequest updateImageVersionRequest)
```
    Updates the properties of a SageMaker image version.
    
    Parameters:
    
    updateImageVersionRequest -
    
    Returns:
    
    Result of the UpdateImageVersion operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - updateInferenceComponent
```
UpdateInferenceComponentResult updateInferenceComponent(UpdateInferenceComponentRequest updateInferenceComponentRequest)
```
    Updates an inference component.
    
    Parameters:
    
    updateInferenceComponentRequest -
    
    Returns:
    
    Result of the UpdateInferenceComponent operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - updateInferenceComponentRuntimeConfig
```
UpdateInferenceComponentRuntimeConfigResult updateInferenceComponentRuntimeConfig(UpdateInferenceComponentRuntimeConfigRequest updateInferenceComponentRuntimeConfigRequest)
```
    Runtime settings for a model that is deployed with an inference component.
    
    Parameters:
    
    updateInferenceComponentRuntimeConfigRequest -
    
    Returns:
    
    Result of the UpdateInferenceComponentRuntimeConfig operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - updateInferenceExperiment
```
UpdateInferenceExperimentResult updateInferenceExperiment(UpdateInferenceExperimentRequest updateInferenceExperimentRequest)
```
    Updates an inference experiment that you created. The status of the inference experiment has to be either Created, Running. For more information on the status of an inference experiment, see DescribeInferenceExperiment.
    
    Parameters:
    
    updateInferenceExperimentRequest -
    
    Returns:
    
    Result of the UpdateInferenceExperiment operation returned by the service.
    
    Throws:
    
    ConflictException - There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - updateMlflowTrackingServer
```
UpdateMlflowTrackingServerResult updateMlflowTrackingServer(UpdateMlflowTrackingServerRequest updateMlflowTrackingServerRequest)
```
    Updates properties of an existing MLflow Tracking Server.
    
    Parameters:
    
    updateMlflowTrackingServerRequest -
    
    Returns:
    
    Result of the UpdateMlflowTrackingServer operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    ConflictException - There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.
    
    See Also:
    
    AWS API Documentation
  - updateModelCard
```
UpdateModelCardResult updateModelCard(UpdateModelCardRequest updateModelCardRequest)
```
    Update an Amazon SageMaker Model Card.
    
    You cannot update both model card content and model card status in a single call.
    
    Parameters:
    
    updateModelCardRequest -
    
    Returns:
    
    Result of the UpdateModelCard operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    ConflictException - There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.
    
    See Also:
    
    AWS API Documentation
  - updateModelPackage
```
UpdateModelPackageResult updateModelPackage(UpdateModelPackageRequest updateModelPackageRequest)
```
    Updates a versioned model.
    
    Parameters:
    
    updateModelPackageRequest -
    
    Returns:
    
    Result of the UpdateModelPackage operation returned by the service.
    
    Throws:
    
    ConflictException - There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.
    
    See Also:
    
    AWS API Documentation
  - updateMonitoringAlert
```
UpdateMonitoringAlertResult updateMonitoringAlert(UpdateMonitoringAlertRequest updateMonitoringAlertRequest)
```
    Update the parameters of a model monitor alert.
    
    Parameters:
    
    updateMonitoringAlertRequest -
    
    Returns:
    
    Result of the UpdateMonitoringAlert operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - updateMonitoringSchedule
```
UpdateMonitoringScheduleResult updateMonitoringSchedule(UpdateMonitoringScheduleRequest updateMonitoringScheduleRequest)
```
    Updates a previously created schedule.
    
    Parameters:
    
    updateMonitoringScheduleRequest -
    
    Returns:
    
    Result of the UpdateMonitoringSchedule operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - updateNotebookInstance
```
UpdateNotebookInstanceResult updateNotebookInstance(UpdateNotebookInstanceRequest updateNotebookInstanceRequest)
```
    Updates a notebook instance. NotebookInstance updates include upgrading or downgrading the ML compute instance used for your notebook instance to accommodate changes in your workload requirements.
    
    Parameters:
    
    updateNotebookInstanceRequest -
    
    Returns:
    
    Result of the UpdateNotebookInstance operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - updateNotebookInstanceLifecycleConfig
```
UpdateNotebookInstanceLifecycleConfigResult updateNotebookInstanceLifecycleConfig(UpdateNotebookInstanceLifecycleConfigRequest updateNotebookInstanceLifecycleConfigRequest)
```
    Updates a notebook instance lifecycle configuration created with the CreateNotebookInstanceLifecycleConfig API.
    
    Parameters:
    
    updateNotebookInstanceLifecycleConfigRequest -
    
    Returns:
    
    Result of the UpdateNotebookInstanceLifecycleConfig operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - updatePipeline
```
UpdatePipelineResult updatePipeline(UpdatePipelineRequest updatePipelineRequest)
```
    Updates a pipeline.
    
    Parameters:
    
    updatePipelineRequest -
    
    Returns:
    
    Result of the UpdatePipeline operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    ConflictException - There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.
    
    See Also:
    
    AWS API Documentation
  - updatePipelineExecution
```
UpdatePipelineExecutionResult updatePipelineExecution(UpdatePipelineExecutionRequest updatePipelineExecutionRequest)
```
    Updates a pipeline execution.
    
    Parameters:
    
    updatePipelineExecutionRequest -
    
    Returns:
    
    Result of the UpdatePipelineExecution operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    ConflictException - There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.
    
    See Also:
    
    AWS API Documentation
  - updateProject
```
UpdateProjectResult updateProject(UpdateProjectRequest updateProjectRequest)
```
    Updates a machine learning (ML) project that is created from a template that sets up an ML pipeline from training to deploying an approved model.
    
    You must not update a project that is in use. If you update the ServiceCatalogProvisioningUpdateDetails of a project that is active or being created, or updated, you may lose resources already created by the project.
    
    Parameters:
    
    updateProjectRequest -
    
    Returns:
    
    Result of the UpdateProject operation returned by the service.
    
    Throws:
    
    ConflictException - There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.
    
    See Also:
    
    AWS API Documentation
  - updateSpace
```
UpdateSpaceResult updateSpace(UpdateSpaceRequest updateSpaceRequest)
```
    Updates the settings of a space.
    
    Parameters:
    
    updateSpaceRequest -
    
    Returns:
    
    Result of the UpdateSpace operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - updateTrainingJob
```
UpdateTrainingJobResult updateTrainingJob(UpdateTrainingJobRequest updateTrainingJobRequest)
```
    Update a model training job to request a new Debugger profiling configuration or to change warm pool retention length.
    
    Parameters:
    
    updateTrainingJobRequest -
    
    Returns:
    
    Result of the UpdateTrainingJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - updateTrial
```
UpdateTrialResult updateTrial(UpdateTrialRequest updateTrialRequest)
```
    Updates the display name of a trial.
    
    Parameters:
    
    updateTrialRequest -
    
    Returns:
    
    Result of the UpdateTrial operation returned by the service.
    
    Throws:
    
    ConflictException - There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - updateTrialComponent
```
UpdateTrialComponentResult updateTrialComponent(UpdateTrialComponentRequest updateTrialComponentRequest)
```
    Updates one or more properties of a trial component.
    
    Parameters:
    
    updateTrialComponentRequest -
    
    Returns:
    
    Result of the UpdateTrialComponent operation returned by the service.
    
    Throws:
    
    ConflictException - There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - updateUserProfile
```
UpdateUserProfileResult updateUserProfile(UpdateUserProfileRequest updateUserProfileRequest)
```
    Updates a user profile.
    
    Parameters:
    
    updateUserProfileRequest -
    
    Returns:
    
    Result of the UpdateUserProfile operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - updateWorkforce
```
UpdateWorkforceResult updateWorkforce(UpdateWorkforceRequest updateWorkforceRequest)
```
    Use this operation to update your workforce. You can use this operation to require that workers use specific IP addresses to work on tasks and to update your OpenID Connect (OIDC) Identity Provider (IdP) workforce configuration.
    
    The worker portal is now supported in VPC and public internet.
    
    Use SourceIpConfig to restrict worker access to tasks to a specific range of IP addresses. You specify allowed IP addresses by creating a list of up to ten CIDRs. By default, a workforce isn't restricted to specific IP addresses. If you specify a range of IP addresses, workers who attempt to access tasks using any IP address outside the specified range are denied and get a Not Found error message on the worker portal.
    
    To restrict access to all the workers in public internet, add the SourceIpConfig CIDR value as "10.0.0.0/16".
    
    Amazon SageMaker does not support Source Ip restriction for worker portals in VPC.
    
    Use OidcConfig to update the configuration of a workforce created using your own OIDC IdP.
    
    You can only update your OIDC IdP configuration when there are no work teams associated with your workforce. You can delete work teams using the DeleteWorkteam operation.
    
    After restricting access to a range of IP addresses or updating your OIDC IdP configuration with this operation, you can view details about your update workforce using the DescribeWorkforce operation.
    
    This operation only applies to private workforces.
    
    Parameters:
    
    updateWorkforceRequest -
    
    Returns:
    
    Result of the UpdateWorkforce operation returned by the service.
    
    Throws:
    
    ConflictException - There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.
    
    See Also:
    
    AWS API Documentation
  - updateWorkteam
```
UpdateWorkteamResult updateWorkteam(UpdateWorkteamRequest updateWorkteamRequest)
```
    Updates an existing work team with new member definitions or description.
    
    Parameters:
    
    updateWorkteamRequest -
    
    Returns:
    
    Result of the UpdateWorkteam operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - shutdown
```
void shutdown()
```
    Shuts down this client object, releasing any resources that might be held open. This is an optional method, and callers are not expected to call it, but can if they want to explicitly release any open resources. Once a client has been shutdown, it should not be used to make any more requests.
  - getCachedResponseMetadata
```
ResponseMetadata getCachedResponseMetadata(AmazonWebServiceRequest request)
```
    Returns additional metadata for a previously executed successful request, typically used for debugging issues where a service isn't acting as expected. This data isn't considered part of the result data returned by an operation, so it's available through this separate, diagnostic interface.
    Response metadata is only cached for a limited period of time, so if you need to access this extra diagnostic information for an executed request, you should use this method to retrieve it as soon as possible after executing a request.
    
    Parameters:
    
    request - The originally executed request.
    
    Returns:
    
    The response metadata for the specified request, or null if none is available.
  - waiters
```
AmazonSageMakerWaiters waiters()
```

AWS SDK for Java 1.x API Reference - 1.12.771

Interface AmazonSageMaker

Field Summary

Method Summary

Field Detail

ENDPOINT_PREFIX

Method Detail

addAssociation

addTags

associateTrialComponent

batchDescribeModelPackage

createAction

createAlgorithm

createApp

createAppImageConfig

createArtifact

createAutoMLJob

createAutoMLJobV2

createCluster

createCodeRepository

createCompilationJob

createContext

createDataQualityJobDefinition

createDeviceFleet

createDomain

createEdgeDeploymentPlan

createEdgeDeploymentStage

createEdgePackagingJob

createEndpoint

createEndpointConfig

createExperiment

createFeatureGroup

createFlowDefinition

createHub

createHubContentReference

createHumanTaskUi

createHyperParameterTuningJob

createImage

createImageVersion

createInferenceComponent

createInferenceExperiment

createInferenceRecommendationsJob

createLabelingJob

createMlflowTrackingServer

createModel

createModelBiasJobDefinition

createModelCard

createModelCardExportJob

createModelExplainabilityJobDefinition

createModelPackage

createModelPackageGroup

createModelQualityJobDefinition

createMonitoringSchedule

createNotebookInstance

createNotebookInstanceLifecycleConfig

createOptimizationJob

createPipeline

createPresignedDomainUrl

createPresignedMlflowTrackingServerUrl

createPresignedNotebookInstanceUrl

createProcessingJob

createProject

createSpace

createStudioLifecycleConfig

createTrainingJob

createTransformJob

createTrial

createTrialComponent

createUserProfile

createWorkforce

createWorkteam

deleteAction

deleteAlgorithm

deleteApp

deleteAppImageConfig

deleteArtifact

deleteAssociation

deleteCluster

deleteCodeRepository

deleteCompilationJob