@Generated(value="com.amazonaws:aws-java-sdk-code-generator") public interface AmazonMachineLearningAsync extends AmazonMachineLearning
AsyncHandler can be used to receive
notification when an asynchronous operation completes.
Note: Do not directly implement this interface, new methods are added to it regularly. Extend from
AbstractAmazonMachineLearningAsync instead.
Definition of the public APIs exposed by Amazon Machine Learning
ENDPOINT_PREFIX| Modifier and Type | Method and Description |
|---|---|
Future<AddTagsResult> |
addTagsAsync(AddTagsRequest addTagsRequest)
Adds one or more tags to an object, up to a limit of 10.
|
Future<AddTagsResult> |
addTagsAsync(AddTagsRequest addTagsRequest,
AsyncHandler<AddTagsRequest,AddTagsResult> asyncHandler)
Adds one or more tags to an object, up to a limit of 10.
|
Future<CreateBatchPredictionResult> |
createBatchPredictionAsync(CreateBatchPredictionRequest createBatchPredictionRequest)
Generates predictions for a group of observations.
|
Future<CreateBatchPredictionResult> |
createBatchPredictionAsync(CreateBatchPredictionRequest createBatchPredictionRequest,
AsyncHandler<CreateBatchPredictionRequest,CreateBatchPredictionResult> asyncHandler)
Generates predictions for a group of observations.
|
Future<CreateDataSourceFromRDSResult> |
createDataSourceFromRDSAsync(CreateDataSourceFromRDSRequest createDataSourceFromRDSRequest)
Creates a
DataSource object from an Amazon Relational Database
Service (Amazon RDS). |
Future<CreateDataSourceFromRDSResult> |
createDataSourceFromRDSAsync(CreateDataSourceFromRDSRequest createDataSourceFromRDSRequest,
AsyncHandler<CreateDataSourceFromRDSRequest,CreateDataSourceFromRDSResult> asyncHandler)
Creates a
DataSource object from an Amazon Relational Database
Service (Amazon RDS). |
Future<CreateDataSourceFromRedshiftResult> |
createDataSourceFromRedshiftAsync(CreateDataSourceFromRedshiftRequest createDataSourceFromRedshiftRequest)
Creates a
DataSource from a database hosted on an Amazon Redshift cluster. |
Future<CreateDataSourceFromRedshiftResult> |
createDataSourceFromRedshiftAsync(CreateDataSourceFromRedshiftRequest createDataSourceFromRedshiftRequest,
AsyncHandler<CreateDataSourceFromRedshiftRequest,CreateDataSourceFromRedshiftResult> asyncHandler)
Creates a
DataSource from a database hosted on an Amazon Redshift cluster. |
Future<CreateDataSourceFromS3Result> |
createDataSourceFromS3Async(CreateDataSourceFromS3Request createDataSourceFromS3Request)
Creates a
DataSource object. |
Future<CreateDataSourceFromS3Result> |
createDataSourceFromS3Async(CreateDataSourceFromS3Request createDataSourceFromS3Request,
AsyncHandler<CreateDataSourceFromS3Request,CreateDataSourceFromS3Result> asyncHandler)
Creates a
DataSource object. |
Future<CreateEvaluationResult> |
createEvaluationAsync(CreateEvaluationRequest createEvaluationRequest)
Creates a new
Evaluation of an MLModel. |
Future<CreateEvaluationResult> |
createEvaluationAsync(CreateEvaluationRequest createEvaluationRequest,
AsyncHandler<CreateEvaluationRequest,CreateEvaluationResult> asyncHandler)
Creates a new
Evaluation of an MLModel. |
Future<CreateMLModelResult> |
createMLModelAsync(CreateMLModelRequest createMLModelRequest)
Creates a new
MLModel using the DataSource and the recipe as information sources. |
Future<CreateMLModelResult> |
createMLModelAsync(CreateMLModelRequest createMLModelRequest,
AsyncHandler<CreateMLModelRequest,CreateMLModelResult> asyncHandler)
Creates a new
MLModel using the DataSource and the recipe as information sources. |
Future<CreateRealtimeEndpointResult> |
createRealtimeEndpointAsync(CreateRealtimeEndpointRequest createRealtimeEndpointRequest)
Creates a real-time endpoint for the
MLModel. |
Future<CreateRealtimeEndpointResult> |
createRealtimeEndpointAsync(CreateRealtimeEndpointRequest createRealtimeEndpointRequest,
AsyncHandler<CreateRealtimeEndpointRequest,CreateRealtimeEndpointResult> asyncHandler)
Creates a real-time endpoint for the
MLModel. |
Future<DeleteBatchPredictionResult> |
deleteBatchPredictionAsync(DeleteBatchPredictionRequest deleteBatchPredictionRequest)
Assigns the DELETED status to a
BatchPrediction, rendering it unusable. |
Future<DeleteBatchPredictionResult> |
deleteBatchPredictionAsync(DeleteBatchPredictionRequest deleteBatchPredictionRequest,
AsyncHandler<DeleteBatchPredictionRequest,DeleteBatchPredictionResult> asyncHandler)
Assigns the DELETED status to a
BatchPrediction, rendering it unusable. |
Future<DeleteDataSourceResult> |
deleteDataSourceAsync(DeleteDataSourceRequest deleteDataSourceRequest)
Assigns the DELETED status to a
DataSource, rendering it unusable. |
Future<DeleteDataSourceResult> |
deleteDataSourceAsync(DeleteDataSourceRequest deleteDataSourceRequest,
AsyncHandler<DeleteDataSourceRequest,DeleteDataSourceResult> asyncHandler)
Assigns the DELETED status to a
DataSource, rendering it unusable. |
Future<DeleteEvaluationResult> |
deleteEvaluationAsync(DeleteEvaluationRequest deleteEvaluationRequest)
Assigns the
DELETED status to an Evaluation, rendering it unusable. |
Future<DeleteEvaluationResult> |
deleteEvaluationAsync(DeleteEvaluationRequest deleteEvaluationRequest,
AsyncHandler<DeleteEvaluationRequest,DeleteEvaluationResult> asyncHandler)
Assigns the
DELETED status to an Evaluation, rendering it unusable. |
Future<DeleteMLModelResult> |
deleteMLModelAsync(DeleteMLModelRequest deleteMLModelRequest)
Assigns the
DELETED status to an MLModel, rendering it unusable. |
Future<DeleteMLModelResult> |
deleteMLModelAsync(DeleteMLModelRequest deleteMLModelRequest,
AsyncHandler<DeleteMLModelRequest,DeleteMLModelResult> asyncHandler)
Assigns the
DELETED status to an MLModel, rendering it unusable. |
Future<DeleteRealtimeEndpointResult> |
deleteRealtimeEndpointAsync(DeleteRealtimeEndpointRequest deleteRealtimeEndpointRequest)
Deletes a real time endpoint of an
MLModel. |
Future<DeleteRealtimeEndpointResult> |
deleteRealtimeEndpointAsync(DeleteRealtimeEndpointRequest deleteRealtimeEndpointRequest,
AsyncHandler<DeleteRealtimeEndpointRequest,DeleteRealtimeEndpointResult> asyncHandler)
Deletes a real time endpoint of an
MLModel. |
Future<DeleteTagsResult> |
deleteTagsAsync(DeleteTagsRequest deleteTagsRequest)
Deletes the specified tags associated with an ML object.
|
Future<DeleteTagsResult> |
deleteTagsAsync(DeleteTagsRequest deleteTagsRequest,
AsyncHandler<DeleteTagsRequest,DeleteTagsResult> asyncHandler)
Deletes the specified tags associated with an ML object.
|
Future<DescribeBatchPredictionsResult> |
describeBatchPredictionsAsync()
Simplified method form for invoking the DescribeBatchPredictions operation.
|
Future<DescribeBatchPredictionsResult> |
describeBatchPredictionsAsync(AsyncHandler<DescribeBatchPredictionsRequest,DescribeBatchPredictionsResult> asyncHandler)
Simplified method form for invoking the DescribeBatchPredictions operation with an AsyncHandler.
|
Future<DescribeBatchPredictionsResult> |
describeBatchPredictionsAsync(DescribeBatchPredictionsRequest describeBatchPredictionsRequest)
Returns a list of
BatchPrediction operations that match the search criteria in the request. |
Future<DescribeBatchPredictionsResult> |
describeBatchPredictionsAsync(DescribeBatchPredictionsRequest describeBatchPredictionsRequest,
AsyncHandler<DescribeBatchPredictionsRequest,DescribeBatchPredictionsResult> asyncHandler)
Returns a list of
BatchPrediction operations that match the search criteria in the request. |
Future<DescribeDataSourcesResult> |
describeDataSourcesAsync()
Simplified method form for invoking the DescribeDataSources operation.
|
Future<DescribeDataSourcesResult> |
describeDataSourcesAsync(AsyncHandler<DescribeDataSourcesRequest,DescribeDataSourcesResult> asyncHandler)
Simplified method form for invoking the DescribeDataSources operation with an AsyncHandler.
|
Future<DescribeDataSourcesResult> |
describeDataSourcesAsync(DescribeDataSourcesRequest describeDataSourcesRequest)
Returns a list of
DataSource that match the search criteria in the request. |
Future<DescribeDataSourcesResult> |
describeDataSourcesAsync(DescribeDataSourcesRequest describeDataSourcesRequest,
AsyncHandler<DescribeDataSourcesRequest,DescribeDataSourcesResult> asyncHandler)
Returns a list of
DataSource that match the search criteria in the request. |
Future<DescribeEvaluationsResult> |
describeEvaluationsAsync()
Simplified method form for invoking the DescribeEvaluations operation.
|
Future<DescribeEvaluationsResult> |
describeEvaluationsAsync(AsyncHandler<DescribeEvaluationsRequest,DescribeEvaluationsResult> asyncHandler)
Simplified method form for invoking the DescribeEvaluations operation with an AsyncHandler.
|
Future<DescribeEvaluationsResult> |
describeEvaluationsAsync(DescribeEvaluationsRequest describeEvaluationsRequest)
Returns a list of
DescribeEvaluations that match the search criteria in the request. |
Future<DescribeEvaluationsResult> |
describeEvaluationsAsync(DescribeEvaluationsRequest describeEvaluationsRequest,
AsyncHandler<DescribeEvaluationsRequest,DescribeEvaluationsResult> asyncHandler)
Returns a list of
DescribeEvaluations that match the search criteria in the request. |
Future<DescribeMLModelsResult> |
describeMLModelsAsync()
Simplified method form for invoking the DescribeMLModels operation.
|
Future<DescribeMLModelsResult> |
describeMLModelsAsync(AsyncHandler<DescribeMLModelsRequest,DescribeMLModelsResult> asyncHandler)
Simplified method form for invoking the DescribeMLModels operation with an AsyncHandler.
|
Future<DescribeMLModelsResult> |
describeMLModelsAsync(DescribeMLModelsRequest describeMLModelsRequest)
Returns a list of
MLModel that match the search criteria in the request. |
Future<DescribeMLModelsResult> |
describeMLModelsAsync(DescribeMLModelsRequest describeMLModelsRequest,
AsyncHandler<DescribeMLModelsRequest,DescribeMLModelsResult> asyncHandler)
Returns a list of
MLModel that match the search criteria in the request. |
Future<DescribeTagsResult> |
describeTagsAsync(DescribeTagsRequest describeTagsRequest)
Describes one or more of the tags for your Amazon ML object.
|
Future<DescribeTagsResult> |
describeTagsAsync(DescribeTagsRequest describeTagsRequest,
AsyncHandler<DescribeTagsRequest,DescribeTagsResult> asyncHandler)
Describes one or more of the tags for your Amazon ML object.
|
Future<GetBatchPredictionResult> |
getBatchPredictionAsync(GetBatchPredictionRequest getBatchPredictionRequest)
Returns a
BatchPrediction that includes detailed metadata, status, and data file information for a
Batch Prediction request. |
Future<GetBatchPredictionResult> |
getBatchPredictionAsync(GetBatchPredictionRequest getBatchPredictionRequest,
AsyncHandler<GetBatchPredictionRequest,GetBatchPredictionResult> asyncHandler)
Returns a
BatchPrediction that includes detailed metadata, status, and data file information for a
Batch Prediction request. |
Future<GetDataSourceResult> |
getDataSourceAsync(GetDataSourceRequest getDataSourceRequest)
Returns a
DataSource that includes metadata and data file information, as well as the current status
of the DataSource. |
Future<GetDataSourceResult> |
getDataSourceAsync(GetDataSourceRequest getDataSourceRequest,
AsyncHandler<GetDataSourceRequest,GetDataSourceResult> asyncHandler)
Returns a
DataSource that includes metadata and data file information, as well as the current status
of the DataSource. |
Future<GetEvaluationResult> |
getEvaluationAsync(GetEvaluationRequest getEvaluationRequest)
Returns an
Evaluation that includes metadata as well as the current status of the
Evaluation. |
Future<GetEvaluationResult> |
getEvaluationAsync(GetEvaluationRequest getEvaluationRequest,
AsyncHandler<GetEvaluationRequest,GetEvaluationResult> asyncHandler)
Returns an
Evaluation that includes metadata as well as the current status of the
Evaluation. |
Future<GetMLModelResult> |
getMLModelAsync(GetMLModelRequest getMLModelRequest)
Returns an
MLModel that includes detailed metadata, data source information, and the current status
of the MLModel. |
Future<GetMLModelResult> |
getMLModelAsync(GetMLModelRequest getMLModelRequest,
AsyncHandler<GetMLModelRequest,GetMLModelResult> asyncHandler)
Returns an
MLModel that includes detailed metadata, data source information, and the current status
of the MLModel. |
Future<PredictResult> |
predictAsync(PredictRequest predictRequest)
Generates a prediction for the observation using the specified
ML Model. |
Future<PredictResult> |
predictAsync(PredictRequest predictRequest,
AsyncHandler<PredictRequest,PredictResult> asyncHandler)
Generates a prediction for the observation using the specified
ML Model. |
Future<UpdateBatchPredictionResult> |
updateBatchPredictionAsync(UpdateBatchPredictionRequest updateBatchPredictionRequest)
Updates the
BatchPredictionName of a BatchPrediction. |
Future<UpdateBatchPredictionResult> |
updateBatchPredictionAsync(UpdateBatchPredictionRequest updateBatchPredictionRequest,
AsyncHandler<UpdateBatchPredictionRequest,UpdateBatchPredictionResult> asyncHandler)
Updates the
BatchPredictionName of a BatchPrediction. |
Future<UpdateDataSourceResult> |
updateDataSourceAsync(UpdateDataSourceRequest updateDataSourceRequest)
Updates the
DataSourceName of a DataSource. |
Future<UpdateDataSourceResult> |
updateDataSourceAsync(UpdateDataSourceRequest updateDataSourceRequest,
AsyncHandler<UpdateDataSourceRequest,UpdateDataSourceResult> asyncHandler)
Updates the
DataSourceName of a DataSource. |
Future<UpdateEvaluationResult> |
updateEvaluationAsync(UpdateEvaluationRequest updateEvaluationRequest)
Updates the
EvaluationName of an Evaluation. |
Future<UpdateEvaluationResult> |
updateEvaluationAsync(UpdateEvaluationRequest updateEvaluationRequest,
AsyncHandler<UpdateEvaluationRequest,UpdateEvaluationResult> asyncHandler)
Updates the
EvaluationName of an Evaluation. |
Future<UpdateMLModelResult> |
updateMLModelAsync(UpdateMLModelRequest updateMLModelRequest)
Updates the
MLModelName and the ScoreThreshold of an MLModel. |
Future<UpdateMLModelResult> |
updateMLModelAsync(UpdateMLModelRequest updateMLModelRequest,
AsyncHandler<UpdateMLModelRequest,UpdateMLModelResult> asyncHandler)
Updates the
MLModelName and the ScoreThreshold of an MLModel. |
addTags, createBatchPrediction, createDataSourceFromRDS, createDataSourceFromRedshift, createDataSourceFromS3, createEvaluation, createMLModel, createRealtimeEndpoint, deleteBatchPrediction, deleteDataSource, deleteEvaluation, deleteMLModel, deleteRealtimeEndpoint, deleteTags, describeBatchPredictions, describeBatchPredictions, describeDataSources, describeDataSources, describeEvaluations, describeEvaluations, describeMLModels, describeMLModels, describeTags, getBatchPrediction, getCachedResponseMetadata, getDataSource, getEvaluation, getMLModel, predict, setEndpoint, setRegion, shutdown, updateBatchPrediction, updateDataSource, updateEvaluation, updateMLModel, waitersFuture<AddTagsResult> addTagsAsync(AddTagsRequest addTagsRequest)
Adds one or more tags to an object, up to a limit of 10. Each tag consists of a key and an optional value. If you
add a tag using a key that is already associated with the ML object, AddTags updates the tag's
value.
addTagsRequest - Future<AddTagsResult> addTagsAsync(AddTagsRequest addTagsRequest, AsyncHandler<AddTagsRequest,AddTagsResult> asyncHandler)
Adds one or more tags to an object, up to a limit of 10. Each tag consists of a key and an optional value. If you
add a tag using a key that is already associated with the ML object, AddTags updates the tag's
value.
addTagsRequest - asyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateBatchPredictionResult> createBatchPredictionAsync(CreateBatchPredictionRequest createBatchPredictionRequest)
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.
createBatchPredictionRequest - Future<CreateBatchPredictionResult> createBatchPredictionAsync(CreateBatchPredictionRequest createBatchPredictionRequest, AsyncHandler<CreateBatchPredictionRequest,CreateBatchPredictionResult> asyncHandler)
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.
createBatchPredictionRequest - asyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateDataSourceFromRDSResult> createDataSourceFromRDSAsync(CreateDataSourceFromRDSRequest createDataSourceFromRDSRequest)
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
the COMPLETED or PENDING state can be used only 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.
createDataSourceFromRDSRequest - Future<CreateDataSourceFromRDSResult> createDataSourceFromRDSAsync(CreateDataSourceFromRDSRequest createDataSourceFromRDSRequest, AsyncHandler<CreateDataSourceFromRDSRequest,CreateDataSourceFromRDSResult> asyncHandler)
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
the COMPLETED or PENDING state can be used only 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.
createDataSourceFromRDSRequest - asyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateDataSourceFromRedshiftResult> createDataSourceFromRedshiftAsync(CreateDataSourceFromRedshiftRequest createDataSourceFromRedshiftRequest)
Creates a DataSource from a database hosted on an Amazon Redshift cluster. 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 states can be used to perform only CreateMLModel,
CreateEvaluation, or CreateBatchPrediction operations.
If Amazon ML can't 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 be contained in the database hosted on an Amazon Redshift cluster and should be specified
by a SelectSqlQuery query. Amazon ML executes an Unload command in Amazon Redshift to
transfer the result set of the SelectSqlQuery query to S3StagingLocation.
After the DataSource has been created, it's ready for use in evaluations and batch predictions. If
you plan to use the DataSource to train an MLModel, the DataSource also
requires a recipe. A recipe describes how each input variable will be used in training an MLModel.
Will the variable be included or excluded from training? Will the variable be manipulated; for example, will it
be combined with another variable or will it be split apart into word combinations? The recipe provides answers
to these questions.
You can't change an existing datasource, but you can copy and modify the settings from an existing Amazon
Redshift datasource to create a new datasource. To do so, call GetDataSource for an existing
datasource and copy the values to a CreateDataSource call. Change the settings that you want to
change and make sure that all required fields have the appropriate values.
createDataSourceFromRedshiftRequest - Future<CreateDataSourceFromRedshiftResult> createDataSourceFromRedshiftAsync(CreateDataSourceFromRedshiftRequest createDataSourceFromRedshiftRequest, AsyncHandler<CreateDataSourceFromRedshiftRequest,CreateDataSourceFromRedshiftResult> asyncHandler)
Creates a DataSource from a database hosted on an Amazon Redshift cluster. 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 states can be used to perform only CreateMLModel,
CreateEvaluation, or CreateBatchPrediction operations.
If Amazon ML can't 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 be contained in the database hosted on an Amazon Redshift cluster and should be specified
by a SelectSqlQuery query. Amazon ML executes an Unload command in Amazon Redshift to
transfer the result set of the SelectSqlQuery query to S3StagingLocation.
After the DataSource has been created, it's ready for use in evaluations and batch predictions. If
you plan to use the DataSource to train an MLModel, the DataSource also
requires a recipe. A recipe describes how each input variable will be used in training an MLModel.
Will the variable be included or excluded from training? Will the variable be manipulated; for example, will it
be combined with another variable or will it be split apart into word combinations? The recipe provides answers
to these questions.
You can't change an existing datasource, but you can copy and modify the settings from an existing Amazon
Redshift datasource to create a new datasource. To do so, call GetDataSource for an existing
datasource and copy the values to a CreateDataSource call. Change the settings that you want to
change and make sure that all required fields have the appropriate values.
createDataSourceFromRedshiftRequest - asyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateDataSourceFromS3Result> createDataSourceFromS3Async(CreateDataSourceFromS3Request createDataSourceFromS3Request)
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 has been created and is
ready for use, Amazon ML sets the Status parameter to COMPLETED.
DataSource in the COMPLETED or PENDING state can be used to perform only
CreateMLModel, CreateEvaluation or CreateBatchPrediction operations.
If Amazon ML can't 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) location, 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 also
needs a recipe. A recipe describes how each input variable will be used in training an MLModel. Will
the variable be included or excluded from training? Will the variable be manipulated; for example, will it be
combined with another variable or will it be split apart into word combinations? The recipe provides answers to
these questions.
createDataSourceFromS3Request - Future<CreateDataSourceFromS3Result> createDataSourceFromS3Async(CreateDataSourceFromS3Request createDataSourceFromS3Request, AsyncHandler<CreateDataSourceFromS3Request,CreateDataSourceFromS3Result> asyncHandler)
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 has been created and is
ready for use, Amazon ML sets the Status parameter to COMPLETED.
DataSource in the COMPLETED or PENDING state can be used to perform only
CreateMLModel, CreateEvaluation or CreateBatchPrediction operations.
If Amazon ML can't 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) location, 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 also
needs a recipe. A recipe describes how each input variable will be used in training an MLModel. Will
the variable be included or excluded from training? Will the variable be manipulated; for example, will it be
combined with another variable or will it be split apart into word combinations? The recipe provides answers to
these questions.
createDataSourceFromS3Request - asyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateEvaluationResult> createEvaluationAsync(CreateEvaluationRequest createEvaluationRequest)
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.
createEvaluationRequest - Future<CreateEvaluationResult> createEvaluationAsync(CreateEvaluationRequest createEvaluationRequest, AsyncHandler<CreateEvaluationRequest,CreateEvaluationResult> asyncHandler)
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.
createEvaluationRequest - asyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateMLModelResult> createMLModelAsync(CreateMLModelRequest createMLModelRequest)
Creates a new MLModel using the DataSource and the recipe as information sources.
An MLModel is nearly immutable. Users can update only 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 has been created and ready is for use, Amazon ML sets the status to
COMPLETED.
You can use the GetMLModel operation to check the 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.
createMLModelRequest - Future<CreateMLModelResult> createMLModelAsync(CreateMLModelRequest createMLModelRequest, AsyncHandler<CreateMLModelRequest,CreateMLModelResult> asyncHandler)
Creates a new MLModel using the DataSource and the recipe as information sources.
An MLModel is nearly immutable. Users can update only 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 has been created and ready is for use, Amazon ML sets the status to
COMPLETED.
You can use the GetMLModel operation to check the 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.
createMLModelRequest - asyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<CreateRealtimeEndpointResult> createRealtimeEndpointAsync(CreateRealtimeEndpointRequest createRealtimeEndpointRequest)
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.
createRealtimeEndpointRequest - Future<CreateRealtimeEndpointResult> createRealtimeEndpointAsync(CreateRealtimeEndpointRequest createRealtimeEndpointRequest, AsyncHandler<CreateRealtimeEndpointRequest,CreateRealtimeEndpointResult> asyncHandler)
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.
createRealtimeEndpointRequest - asyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DeleteBatchPredictionResult> deleteBatchPredictionAsync(DeleteBatchPredictionRequest deleteBatchPredictionRequest)
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.
deleteBatchPredictionRequest - Future<DeleteBatchPredictionResult> deleteBatchPredictionAsync(DeleteBatchPredictionRequest deleteBatchPredictionRequest, AsyncHandler<DeleteBatchPredictionRequest,DeleteBatchPredictionResult> asyncHandler)
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.
deleteBatchPredictionRequest - asyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DeleteDataSourceResult> deleteDataSourceAsync(DeleteDataSourceRequest deleteDataSourceRequest)
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.
deleteDataSourceRequest - Future<DeleteDataSourceResult> deleteDataSourceAsync(DeleteDataSourceRequest deleteDataSourceRequest, AsyncHandler<DeleteDataSourceRequest,DeleteDataSourceResult> asyncHandler)
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.
deleteDataSourceRequest - asyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DeleteEvaluationResult> deleteEvaluationAsync(DeleteEvaluationRequest deleteEvaluationRequest)
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.
deleteEvaluationRequest - Future<DeleteEvaluationResult> deleteEvaluationAsync(DeleteEvaluationRequest deleteEvaluationRequest, AsyncHandler<DeleteEvaluationRequest,DeleteEvaluationResult> asyncHandler)
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.
deleteEvaluationRequest - asyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DeleteMLModelResult> deleteMLModelAsync(DeleteMLModelRequest deleteMLModelRequest)
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.
deleteMLModelRequest - Future<DeleteMLModelResult> deleteMLModelAsync(DeleteMLModelRequest deleteMLModelRequest, AsyncHandler<DeleteMLModelRequest,DeleteMLModelResult> asyncHandler)
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.
deleteMLModelRequest - asyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DeleteRealtimeEndpointResult> deleteRealtimeEndpointAsync(DeleteRealtimeEndpointRequest deleteRealtimeEndpointRequest)
Deletes a real time endpoint of an MLModel.
deleteRealtimeEndpointRequest - Future<DeleteRealtimeEndpointResult> deleteRealtimeEndpointAsync(DeleteRealtimeEndpointRequest deleteRealtimeEndpointRequest, AsyncHandler<DeleteRealtimeEndpointRequest,DeleteRealtimeEndpointResult> asyncHandler)
Deletes a real time endpoint of an MLModel.
deleteRealtimeEndpointRequest - asyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DeleteTagsResult> deleteTagsAsync(DeleteTagsRequest deleteTagsRequest)
Deletes the specified tags associated with an ML object. After this operation is complete, you can't recover deleted tags.
If you specify a tag that doesn't exist, Amazon ML ignores it.
deleteTagsRequest - Future<DeleteTagsResult> deleteTagsAsync(DeleteTagsRequest deleteTagsRequest, AsyncHandler<DeleteTagsRequest,DeleteTagsResult> asyncHandler)
Deletes the specified tags associated with an ML object. After this operation is complete, you can't recover deleted tags.
If you specify a tag that doesn't exist, Amazon ML ignores it.
deleteTagsRequest - asyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DescribeBatchPredictionsResult> describeBatchPredictionsAsync(DescribeBatchPredictionsRequest describeBatchPredictionsRequest)
Returns a list of BatchPrediction operations that match the search criteria in the request.
describeBatchPredictionsRequest - Future<DescribeBatchPredictionsResult> describeBatchPredictionsAsync(DescribeBatchPredictionsRequest describeBatchPredictionsRequest, AsyncHandler<DescribeBatchPredictionsRequest,DescribeBatchPredictionsResult> asyncHandler)
Returns a list of BatchPrediction operations that match the search criteria in the request.
describeBatchPredictionsRequest - asyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DescribeBatchPredictionsResult> describeBatchPredictionsAsync()
Future<DescribeBatchPredictionsResult> describeBatchPredictionsAsync(AsyncHandler<DescribeBatchPredictionsRequest,DescribeBatchPredictionsResult> asyncHandler)
Future<DescribeDataSourcesResult> describeDataSourcesAsync(DescribeDataSourcesRequest describeDataSourcesRequest)
Returns a list of DataSource that match the search criteria in the request.
describeDataSourcesRequest - Future<DescribeDataSourcesResult> describeDataSourcesAsync(DescribeDataSourcesRequest describeDataSourcesRequest, AsyncHandler<DescribeDataSourcesRequest,DescribeDataSourcesResult> asyncHandler)
Returns a list of DataSource that match the search criteria in the request.
describeDataSourcesRequest - asyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DescribeDataSourcesResult> describeDataSourcesAsync()
Future<DescribeDataSourcesResult> describeDataSourcesAsync(AsyncHandler<DescribeDataSourcesRequest,DescribeDataSourcesResult> asyncHandler)
Future<DescribeEvaluationsResult> describeEvaluationsAsync(DescribeEvaluationsRequest describeEvaluationsRequest)
Returns a list of DescribeEvaluations that match the search criteria in the request.
describeEvaluationsRequest - Future<DescribeEvaluationsResult> describeEvaluationsAsync(DescribeEvaluationsRequest describeEvaluationsRequest, AsyncHandler<DescribeEvaluationsRequest,DescribeEvaluationsResult> asyncHandler)
Returns a list of DescribeEvaluations that match the search criteria in the request.
describeEvaluationsRequest - asyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DescribeEvaluationsResult> describeEvaluationsAsync()
Future<DescribeEvaluationsResult> describeEvaluationsAsync(AsyncHandler<DescribeEvaluationsRequest,DescribeEvaluationsResult> asyncHandler)
Future<DescribeMLModelsResult> describeMLModelsAsync(DescribeMLModelsRequest describeMLModelsRequest)
Returns a list of MLModel that match the search criteria in the request.
describeMLModelsRequest - Future<DescribeMLModelsResult> describeMLModelsAsync(DescribeMLModelsRequest describeMLModelsRequest, AsyncHandler<DescribeMLModelsRequest,DescribeMLModelsResult> asyncHandler)
Returns a list of MLModel that match the search criteria in the request.
describeMLModelsRequest - asyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<DescribeMLModelsResult> describeMLModelsAsync()
Future<DescribeMLModelsResult> describeMLModelsAsync(AsyncHandler<DescribeMLModelsRequest,DescribeMLModelsResult> asyncHandler)
Future<DescribeTagsResult> describeTagsAsync(DescribeTagsRequest describeTagsRequest)
Describes one or more of the tags for your Amazon ML object.
describeTagsRequest - Future<DescribeTagsResult> describeTagsAsync(DescribeTagsRequest describeTagsRequest, AsyncHandler<DescribeTagsRequest,DescribeTagsResult> asyncHandler)
Describes one or more of the tags for your Amazon ML object.
describeTagsRequest - asyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<GetBatchPredictionResult> getBatchPredictionAsync(GetBatchPredictionRequest getBatchPredictionRequest)
Returns a BatchPrediction that includes detailed metadata, status, and data file information for a
Batch Prediction request.
getBatchPredictionRequest - Future<GetBatchPredictionResult> getBatchPredictionAsync(GetBatchPredictionRequest getBatchPredictionRequest, AsyncHandler<GetBatchPredictionRequest,GetBatchPredictionResult> asyncHandler)
Returns a BatchPrediction that includes detailed metadata, status, and data file information for a
Batch Prediction request.
getBatchPredictionRequest - asyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<GetDataSourceResult> getDataSourceAsync(GetDataSourceRequest getDataSourceRequest)
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.
getDataSourceRequest - Future<GetDataSourceResult> getDataSourceAsync(GetDataSourceRequest getDataSourceRequest, AsyncHandler<GetDataSourceRequest,GetDataSourceResult> asyncHandler)
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.
getDataSourceRequest - asyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<GetEvaluationResult> getEvaluationAsync(GetEvaluationRequest getEvaluationRequest)
Returns an Evaluation that includes metadata as well as the current status of the
Evaluation.
getEvaluationRequest - Future<GetEvaluationResult> getEvaluationAsync(GetEvaluationRequest getEvaluationRequest, AsyncHandler<GetEvaluationRequest,GetEvaluationResult> asyncHandler)
Returns an Evaluation that includes metadata as well as the current status of the
Evaluation.
getEvaluationRequest - asyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<GetMLModelResult> getMLModelAsync(GetMLModelRequest getMLModelRequest)
Returns an MLModel that includes detailed metadata, data source information, and the current status
of the MLModel.
GetMLModel provides results in normal or verbose format.
getMLModelRequest - Future<GetMLModelResult> getMLModelAsync(GetMLModelRequest getMLModelRequest, AsyncHandler<GetMLModelRequest,GetMLModelResult> asyncHandler)
Returns an MLModel that includes detailed metadata, data source information, and the current status
of the MLModel.
GetMLModel provides results in normal or verbose format.
getMLModelRequest - asyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<PredictResult> predictAsync(PredictRequest predictRequest)
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.
predictRequest - Future<PredictResult> predictAsync(PredictRequest predictRequest, AsyncHandler<PredictRequest,PredictResult> asyncHandler)
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.
predictRequest - asyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<UpdateBatchPredictionResult> updateBatchPredictionAsync(UpdateBatchPredictionRequest updateBatchPredictionRequest)
Updates the BatchPredictionName of a BatchPrediction.
You can use the GetBatchPrediction operation to view the contents of the updated data element.
updateBatchPredictionRequest - Future<UpdateBatchPredictionResult> updateBatchPredictionAsync(UpdateBatchPredictionRequest updateBatchPredictionRequest, AsyncHandler<UpdateBatchPredictionRequest,UpdateBatchPredictionResult> asyncHandler)
Updates the BatchPredictionName of a BatchPrediction.
You can use the GetBatchPrediction operation to view the contents of the updated data element.
updateBatchPredictionRequest - asyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<UpdateDataSourceResult> updateDataSourceAsync(UpdateDataSourceRequest updateDataSourceRequest)
Updates the DataSourceName of a DataSource.
You can use the GetDataSource operation to view the contents of the updated data element.
updateDataSourceRequest - Future<UpdateDataSourceResult> updateDataSourceAsync(UpdateDataSourceRequest updateDataSourceRequest, AsyncHandler<UpdateDataSourceRequest,UpdateDataSourceResult> asyncHandler)
Updates the DataSourceName of a DataSource.
You can use the GetDataSource operation to view the contents of the updated data element.
updateDataSourceRequest - asyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<UpdateEvaluationResult> updateEvaluationAsync(UpdateEvaluationRequest updateEvaluationRequest)
Updates the EvaluationName of an Evaluation.
You can use the GetEvaluation operation to view the contents of the updated data element.
updateEvaluationRequest - Future<UpdateEvaluationResult> updateEvaluationAsync(UpdateEvaluationRequest updateEvaluationRequest, AsyncHandler<UpdateEvaluationRequest,UpdateEvaluationResult> asyncHandler)
Updates the EvaluationName of an Evaluation.
You can use the GetEvaluation operation to view the contents of the updated data element.
updateEvaluationRequest - asyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.Future<UpdateMLModelResult> updateMLModelAsync(UpdateMLModelRequest updateMLModelRequest)
Updates the MLModelName and the ScoreThreshold of an MLModel.
You can use the GetMLModel operation to view the contents of the updated data element.
updateMLModelRequest - Future<UpdateMLModelResult> updateMLModelAsync(UpdateMLModelRequest updateMLModelRequest, AsyncHandler<UpdateMLModelRequest,UpdateMLModelResult> asyncHandler)
Updates the MLModelName and the ScoreThreshold of an MLModel.
You can use the GetMLModel operation to view the contents of the updated data element.
updateMLModelRequest - asyncHandler - Asynchronous callback handler for events in the lifecycle of the request. Users can provide an
implementation of the callback methods in this interface to receive notification of successful or
unsuccessful completion of the operation.