AWS SDK Version 2 for .NET
API Reference

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.NET Framework 3.5
 
 

Classes

NameDescription
Class BatchPrediction Represents the output of GetBatchPrediction operation.

The content consists of the detailed metadata, the status, and the data file information of a Batch Prediction.

Class CreateBatchPredictionRequest Container for the parameters to the CreateBatchPrediction operation. Generates predictions for a group of observations. The observations to process exist in one or more data files referenced by a DataSource. This operation creates a new BatchPrediction, and uses an MLModel and the data files referenced by the DataSource as information sources.

CreateBatchPrediction is an asynchronous operation. In response to CreateBatchPrediction, Amazon Machine Learning (Amazon ML) immediately returns and sets the BatchPrediction status to PENDING. After the BatchPrediction completes, Amazon ML sets the status to COMPLETED.

You can poll for status updates by using the GetBatchPrediction operation and checking the Status parameter of the result. After the COMPLETED status appears, the results are available in the location specified by the OutputUri parameter.

Class CreateBatchPredictionResponse Configuration for accessing Amazon CreateBatchPrediction service
Class CreateBatchPredictionResult Represents the output of a CreateBatchPrediction operation, and is an acknowledgement that Amazon ML received the request.

The CreateBatchPrediction operation is asynchronous. You can poll for status updates by using the GetBatchPrediction operation and checking the Status parameter of the result.

Class CreateDataSourceFromRDSRequest Container for the parameters to the CreateDataSourceFromRDS operation. Creates a DataSource object from an Amazon Relational Database Service (Amazon RDS). A DataSource references data that can be used to perform CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.

CreateDataSourceFromRDS is an asynchronous operation. In response to CreateDataSourceFromRDS, Amazon Machine Learning (Amazon ML) immediately returns and sets the DataSource status to PENDING. After the DataSource is created and ready for use, Amazon ML sets the Status parameter to COMPLETED. DataSource in COMPLETED or PENDING status can only be used to perform CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.

If Amazon ML cannot accept the input source, it sets the Status parameter to FAILED and includes an error message in the Message attribute of the GetDataSource operation response.

Class CreateDataSourceFromRDSResponse Configuration for accessing Amazon CreateDataSourceFromRDS service
Class CreateDataSourceFromRDSResult Represents the output of a CreateDataSourceFromRDS operation, and is an acknowledgement that Amazon ML received the request.

The CreateDataSourceFromRDS operation is asynchronous. You can poll for updates by using the GetBatchPrediction operation and checking the Status parameter. You can inspect the Message when Status shows up as FAILED. You can also check the progress of the copy operation by going to the DataPipeline console and looking up the pipeline using the pipelineId from the describe call.

Class CreateDataSourceFromRedshiftRequest Container for the parameters to the CreateDataSourceFromRedshift operation. Creates a DataSource from Amazon Redshift. A DataSource references data that can be used to perform either CreateMLModel, CreateEvaluation or CreateBatchPrediction operations.

CreateDataSourceFromRedshift is an asynchronous operation. In response to CreateDataSourceFromRedshift, Amazon Machine Learning (Amazon ML) immediately returns and sets the DataSource status to PENDING. After the DataSource is created and ready for use, Amazon ML sets the Status parameter to COMPLETED. DataSource in COMPLETED or PENDING status can only be used to perform CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.

If Amazon ML cannot accept the input source, it sets the Status parameter to FAILED and includes an error message in the Message attribute of the GetDataSource operation response.

The observations should exist in the database hosted on an Amazon Redshift cluster and should be specified by a SelectSqlQuery. Amazon ML executes Unload command in Amazon Redshift to transfer the result set of SelectSqlQuery to S3StagingLocation.

After the DataSource is created, it's ready for use in evaluations and batch predictions. If you plan to use the DataSource to train an MLModel, the DataSource requires another item -- a recipe. A recipe describes the observation variables that participate in training an MLModel. A recipe describes how each input variable will be used in training. Will the variable be included or excluded from training? Will the variable be manipulated, for example, combined with another variable or split apart into word combinations? The recipe provides answers to these questions. For more information, see the Amazon Machine Learning Developer Guide.

Class CreateDataSourceFromRedshiftResponse Configuration for accessing Amazon CreateDataSourceFromRedshift service
Class CreateDataSourceFromRedshiftResult Represents the output of a CreateDataSourceFromRedshift operation, and is an acknowledgement that Amazon ML received the request.

The CreateDataSourceFromRedshift operation is asynchronous. You can poll for updates by using the GetBatchPrediction operation and checking the Status parameter.

Class CreateDataSourceFromS3Request Container for the parameters to the CreateDataSourceFromS3 operation. Creates a DataSource object. A DataSource references data that can be used to perform CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.

CreateDataSourceFromS3 is an asynchronous operation. In response to CreateDataSourceFromS3, Amazon Machine Learning (Amazon ML) immediately returns and sets the DataSource status to PENDING. After the DataSource is created and ready for use, Amazon ML sets the Status parameter to COMPLETED. DataSource in COMPLETED or PENDING status can only be used to perform CreateMLModel, CreateEvaluation or CreateBatchPrediction operations.

If Amazon ML cannot accept the input source, it sets the Status parameter to FAILED and includes an error message in the Message attribute of the GetDataSource operation response.

The observation data used in a DataSource should be ready to use; that is, it should have a consistent structure, and missing data values should be kept to a minimum. The observation data must reside in one or more CSV files in an Amazon Simple Storage Service (Amazon S3) bucket, along with a schema that describes the data items by name and type. The same schema must be used for all of the data files referenced by the DataSource.

After the DataSource has been created, it's ready to use in evaluations and batch predictions. If you plan to use the DataSource to train an MLModel, the DataSource requires another item: a recipe. A recipe describes the observation variables that participate in training an MLModel. A recipe describes how each input variable will be used in training. Will the variable be included or excluded from training? Will the variable be manipulated, for example, combined with another variable, or split apart into word combinations? The recipe provides answers to these questions. For more information, see the Amazon Machine Learning Developer Guide.

Class CreateDataSourceFromS3Response Configuration for accessing Amazon CreateDataSourceFromS3 service
Class CreateDataSourceFromS3Result Represents the output of a CreateDataSourceFromS3 operation, and is an acknowledgement that Amazon ML received the request.

The CreateDataSourceFromS3 operation is asynchronous. You can poll for updates by using the GetBatchPrediction operation and checking the Status parameter.

Class CreateEvaluationRequest Container for the parameters to the CreateEvaluation operation. Creates a new Evaluation of an MLModel. An MLModel is evaluated on a set of observations associated to a DataSource. Like a DataSource for an MLModel, the DataSource for an Evaluation contains values for the Target Variable. The Evaluation compares the predicted result for each observation to the actual outcome and provides a summary so that you know how effective the MLModel functions on the test data. Evaluation generates a relevant performance metric such as BinaryAUC, RegressionRMSE or MulticlassAvgFScore based on the corresponding MLModelType: BINARY, REGRESSION or MULTICLASS.

CreateEvaluation is an asynchronous operation. In response to CreateEvaluation, Amazon Machine Learning (Amazon ML) immediately returns and sets the evaluation status to PENDING. After the Evaluation is created and ready for use, Amazon ML sets the status to COMPLETED.

You can use the GetEvaluation operation to check progress of the evaluation during the creation operation.

Class CreateEvaluationResponse Configuration for accessing Amazon CreateEvaluation service
Class CreateEvaluationResult Represents the output of a CreateEvaluation operation, and is an acknowledgement that Amazon ML received the request.

CreateEvaluation operation is asynchronous. You can poll for status updates by using the GetEvaluation operation and checking the Status parameter.

Class CreateMLModelRequest Container for the parameters to the CreateMLModel operation. Creates a new MLModel using the data files and the recipe as information sources.

An MLModel is nearly immutable. Users can only update the MLModelName and the ScoreThreshold in an MLModel without creating a new MLModel.

CreateMLModel is an asynchronous operation. In response to CreateMLModel, Amazon Machine Learning (Amazon ML) immediately returns and sets the MLModel status to PENDING. After the MLModel is created and ready for use, Amazon ML sets the status to COMPLETED.

You can use the GetMLModel operation to check progress of the MLModel during the creation operation.

CreateMLModel requires a DataSource with computed statistics, which can be created by setting ComputeStatistics to true in CreateDataSourceFromRDS, CreateDataSourceFromS3, or CreateDataSourceFromRedshift operations.

Class CreateMLModelResponse Configuration for accessing Amazon CreateMLModel service
Class CreateMLModelResult Represents the output of a CreateMLModel operation, and is an acknowledgement that Amazon ML received the request.

The CreateMLModel operation is asynchronous. You can poll for status updates by using the GetMLModel operation and checking the Status parameter.

Class CreateRealtimeEndpointRequest Container for the parameters to the CreateRealtimeEndpoint operation. Creates a real-time endpoint for the MLModel. The endpoint contains the URI of the MLModel; that is, the location to send real-time prediction requests for the specified MLModel.
Class CreateRealtimeEndpointResponse Configuration for accessing Amazon CreateRealtimeEndpoint service
Class CreateRealtimeEndpointResult Represents the output of an CreateRealtimeEndpoint operation.

The result contains the MLModelId and the endpoint information for the MLModel.

The endpoint information includes the URI of the MLModel; that is, the location to send online prediction requests for the specified MLModel.

Class DataSource Represents the output of the GetDataSource operation.

The content consists of the detailed metadata and data file information and the current status of the DataSource.

Class DeleteBatchPredictionRequest Container for the parameters to the DeleteBatchPrediction operation. Assigns the DELETED status to a BatchPrediction, rendering it unusable.

After using the DeleteBatchPrediction operation, you can use the GetBatchPrediction operation to verify that the status of the BatchPrediction changed to DELETED.

Caution: The result of the DeleteBatchPrediction operation is irreversible.

Class DeleteBatchPredictionResponse Configuration for accessing Amazon DeleteBatchPrediction service
Class DeleteBatchPredictionResult Represents the output of a DeleteBatchPrediction operation.

You can use the GetBatchPrediction operation and check the value of the Status parameter to see whether a BatchPrediction is marked as DELETED.

Class DeleteDataSourceRequest Container for the parameters to the DeleteDataSource operation. Assigns the DELETED status to a DataSource, rendering it unusable.

After using the DeleteDataSource operation, you can use the GetDataSource operation to verify that the status of the DataSource changed to DELETED.

Caution: The results of the DeleteDataSource operation are irreversible.

Class DeleteDataSourceResponse Configuration for accessing Amazon DeleteDataSource service
Class DeleteDataSourceResult Represents the output of a DeleteDataSource operation.
Class DeleteEvaluationRequest Container for the parameters to the DeleteEvaluation operation. Assigns the DELETED status to an Evaluation, rendering it unusable.

After invoking the DeleteEvaluation operation, you can use the GetEvaluation operation to verify that the status of the Evaluation changed to DELETED.

Caution: The results of the DeleteEvaluation operation are irreversible.

Class DeleteEvaluationResponse Configuration for accessing Amazon DeleteEvaluation service
Class DeleteEvaluationResult Represents the output of a DeleteEvaluation operation. The output indicates that Amazon Machine Learning (Amazon ML) received the request.

You can use the GetEvaluation operation and check the value of the Status parameter to see whether an Evaluation is marked as DELETED.

Class DeleteMLModelRequest Container for the parameters to the DeleteMLModel operation. Assigns the DELETED status to an MLModel, rendering it unusable.

After using the DeleteMLModel operation, you can use the GetMLModel operation to verify that the status of the MLModel changed to DELETED.

Caution: The result of the DeleteMLModel operation is irreversible.

Class DeleteMLModelResponse Configuration for accessing Amazon DeleteMLModel service
Class DeleteMLModelResult Represents the output of a DeleteMLModel operation.

You can use the GetMLModel operation and check the value of the Status parameter to see whether an MLModel is marked as DELETED.

Class DeleteRealtimeEndpointRequest Container for the parameters to the DeleteRealtimeEndpoint operation. Deletes a real time endpoint of an MLModel.
Class DeleteRealtimeEndpointResponse Configuration for accessing Amazon DeleteRealtimeEndpoint service
Class DeleteRealtimeEndpointResult Represents the output of an DeleteRealtimeEndpoint operation.

The result contains the MLModelId and the endpoint information for the MLModel.

Class DescribeBatchPredictionsRequest Container for the parameters to the DescribeBatchPredictions operation. Returns a list of BatchPrediction operations that match the search criteria in the request.
Class DescribeBatchPredictionsResponse Configuration for accessing Amazon DescribeBatchPredictions service
Class DescribeBatchPredictionsResult Represents the output of a DescribeBatchPredictions operation. The content is essentially a list of BatchPredictions.
Class DescribeDataSourcesRequest Container for the parameters to the DescribeDataSources operation. Returns a list of DataSource that match the search criteria in the request.
Class DescribeDataSourcesResponse Configuration for accessing Amazon DescribeDataSources service
Class DescribeDataSourcesResult Represents the query results from a DescribeDataSources operation. The content is essentially a list of DataSource.
Class DescribeEvaluationsRequest Container for the parameters to the DescribeEvaluations operation. Returns a list of DescribeEvaluations that match the search criteria in the request.
Class DescribeEvaluationsResponse Configuration for accessing Amazon DescribeEvaluations service
Class DescribeEvaluationsResult Represents the query results from a DescribeEvaluations operation. The content is essentially a list of Evaluation.
Class DescribeMLModelsRequest Container for the parameters to the DescribeMLModels operation. Returns a list of MLModel that match the search criteria in the request.
Class DescribeMLModelsResponse Configuration for accessing Amazon DescribeMLModels service
Class DescribeMLModelsResult Represents the output of a DescribeMLModels operation. The content is essentially a list of MLModel.
Class Evaluation Represents the output of GetEvaluation operation.

The content consists of the detailed metadata and data file information and the current status of the Evaluation.

Class GetBatchPredictionRequest Container for the parameters to the GetBatchPrediction operation. Returns a BatchPrediction that includes detailed metadata, status, and data file information for a Batch Prediction request.
Class GetBatchPredictionResponse Configuration for accessing Amazon GetBatchPrediction service
Class GetBatchPredictionResult Represents the output of a GetBatchPrediction operation and describes a BatchPrediction.
Class GetDataSourceRequest Container for the parameters to the GetDataSource operation. Returns a DataSource that includes metadata and data file information, as well as the current status of the DataSource.

GetDataSource provides results in normal or verbose format. The verbose format adds the schema description and the list of files pointed to by the DataSource to the normal format.

Class GetDataSourceResponse Configuration for accessing Amazon GetDataSource service
Class GetDataSourceResult Represents the output of a GetDataSource operation and describes a DataSource.
Class GetEvaluationRequest Container for the parameters to the GetEvaluation operation. Returns an Evaluation that includes metadata as well as the current status of the Evaluation.
Class GetEvaluationResponse Configuration for accessing Amazon GetEvaluation service
Class GetEvaluationResult Represents the output of a GetEvaluation operation and describes an Evaluation.
Class GetMLModelRequest Container for the parameters to the GetMLModel operation. Returns an MLModel that includes detailed metadata, and data source information as well as the current status of the MLModel.

GetMLModel provides results in normal or verbose format.

Class GetMLModelResponse Configuration for accessing Amazon GetMLModel service
Class GetMLModelResult Represents the output of a GetMLModel operation, and provides detailed information about a MLModel.
Class IdempotentParameterMismatchException MachineLearning exception
Class InternalServerException MachineLearning exception
Class InvalidInputException MachineLearning exception
Class LimitExceededException MachineLearning exception
Class MLModel Represents the output of a GetMLModel operation.

The content consists of the detailed metadata and the current status of the MLModel.

Class PerformanceMetrics Measurements of how well the MLModel performed on known observations. One of the following metrics is returned, based on the type of the MLModel:

  • BinaryAUC: The binary MLModel uses the Area Under the Curve (AUC) technique to measure performance.

  • RegressionRMSE: The regression MLModel uses the Root Mean Square Error (RMSE) technique to measure performance. RMSE measures the difference between predicted and actual values for a single variable.

  • MulticlassAvgFScore: The multiclass MLModel uses the F1 score technique to measure performance.

For more information about performance metrics, please see the Amazon Machine Learning Developer Guide.

Class Prediction The output from a Predict operation:

  • Details - Contains the following attributes: DetailsAttributes.PREDICTIVE_MODEL_TYPE - REGRESSION | BINARY | MULTICLASS DetailsAttributes.ALGORITHM - SGD

  • PredictedLabel - Present for either a BINARY or MULTICLASS MLModel request.

  • PredictedScores - Contains the raw classification score corresponding to each label.

  • PredictedValue - Present for a REGRESSION MLModel request.

Class PredictorNotMountedException MachineLearning exception
Class PredictRequest Container for the parameters to the Predict operation. Generates a prediction for the observation using the specified ML Model. Note

Not all response parameters will be populated. Whether a response parameter is populated depends on the type of model requested.

Class PredictResponse Configuration for accessing Amazon Predict service
Class PredictResult
Class RDSDatabase The database details of an Amazon RDS database.
Class RDSDatabaseCredentials The database credentials to connect to a database on an RDS DB instance.
Class RDSDataSpec The data specification of an Amazon Relational Database Service (Amazon RDS) DataSource.
Class RDSMetadata The datasource details that are specific to Amazon RDS.
Class RealtimeEndpointInfo Describes the real-time endpoint information for an MLModel.
Class RedshiftDatabase Describes the database details required to connect to an Amazon Redshift database.
Class RedshiftDatabaseCredentials Describes the database credentials for connecting to a database on an Amazon Redshift cluster.
Class RedshiftDataSpec Describes the data specification of an Amazon Redshift DataSource.
Class RedshiftMetadata Describes the DataSource details specific to Amazon Redshift.
Class ResourceNotFoundException MachineLearning exception
Class S3DataSpec Describes the data specification of a DataSource.
Class UpdateBatchPredictionRequest Container for the parameters to the UpdateBatchPrediction operation. Updates the BatchPredictionName of a BatchPrediction.

You can use the GetBatchPrediction operation to view the contents of the updated data element.

Class UpdateBatchPredictionResponse Configuration for accessing Amazon UpdateBatchPrediction service
Class UpdateBatchPredictionResult Represents the output of an UpdateBatchPrediction operation.

You can see the updated content by using the GetBatchPrediction operation.

Class UpdateDataSourceRequest Container for the parameters to the UpdateDataSource operation. Updates the DataSourceName of a DataSource.

You can use the GetDataSource operation to view the contents of the updated data element.

Class UpdateDataSourceResponse Configuration for accessing Amazon UpdateDataSource service
Class UpdateDataSourceResult Represents the output of an UpdateDataSource operation.

You can see the updated content by using the GetBatchPrediction operation.

Class UpdateEvaluationRequest Container for the parameters to the UpdateEvaluation operation. Updates the EvaluationName of an Evaluation.

You can use the GetEvaluation operation to view the contents of the updated data element.

Class UpdateEvaluationResponse Configuration for accessing Amazon UpdateEvaluation service
Class UpdateEvaluationResult Represents the output of an UpdateEvaluation operation.

You can see the updated content by using the GetEvaluation operation.

Class UpdateMLModelRequest Container for the parameters to the UpdateMLModel operation. Updates the MLModelName and the ScoreThreshold of an MLModel.

You can use the GetMLModel operation to view the contents of the updated data element.

Class UpdateMLModelResponse Configuration for accessing Amazon UpdateMLModel service
Class UpdateMLModelResult Represents the output of an UpdateMLModel operation.

You can see the updated content by using the GetMLModel operation.