Package com.amazonaws.services.machinelearning

Interface AmazonMachineLearningAsync

  • All Superinterfaces:
    AmazonMachineLearning
    All Known Implementing Classes:
    AbstractAmazonMachineLearningAsync, AmazonMachineLearningAsyncClient
    public interface AmazonMachineLearningAsync
extends AmazonMachineLearning
    Interface for accessing Amazon Machine Learning asynchronously. Each asynchronous method will return a Java Future object representing the asynchronous operation; overloads which accept an AsyncHandler can be used to receive notification when an asynchronous operation completes.

    Definition of the public APIs exposed by Amazon Machine Learning

    • Method Detail

      • createBatchPredictionAsync

        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.

        Parameters:
        createBatchPredictionRequest -
        Returns:
        A Java Future containing the result of the CreateBatchPrediction operation returned by the service.

      • createBatchPredictionAsync

        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.

        Parameters:
        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.
        Returns:
        A Java Future containing the result of the CreateBatchPrediction operation returned by the service.

      • createDataSourceFromRDSAsync

        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 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.

        Parameters:
        createDataSourceFromRDSRequest -
        Returns:
        A Java Future containing the result of the CreateDataSourceFromRDS operation returned by the service.

      • createDataSourceFromRDSAsync

        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 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.

        Parameters:
        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.
        Returns:
        A Java Future containing the result of the CreateDataSourceFromRDS operation returned by the service.

      • createDataSourceFromRedshiftAsync

        Future<CreateDataSourceFromRedshiftResult> createDataSourceFromRedshiftAsync​(CreateDataSourceFromRedshiftRequest createDataSourceFromRedshiftRequest)

        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.

        Parameters:
        createDataSourceFromRedshiftRequest -
        Returns:
        A Java Future containing the result of the CreateDataSourceFromRedshift operation returned by the service.

      • createDataSourceFromRedshiftAsync

        Future<CreateDataSourceFromRedshiftResult> createDataSourceFromRedshiftAsync​(CreateDataSourceFromRedshiftRequest createDataSourceFromRedshiftRequest,
                                                                             AsyncHandler<CreateDataSourceFromRedshiftRequest,​CreateDataSourceFromRedshiftResult> asyncHandler)

        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.

        Parameters:
        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.
        Returns:
        A Java Future containing the result of the CreateDataSourceFromRedshift operation returned by the service.

      • createDataSourceFromS3Async

        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 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.

        Parameters:
        createDataSourceFromS3Request -
        Returns:
        A Java Future containing the result of the CreateDataSourceFromS3 operation returned by the service.

      • createDataSourceFromS3Async

        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 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.

        Parameters:
        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.
        Returns:
        A Java Future containing the result of the CreateDataSourceFromS3 operation returned by the service.

      • createEvaluationAsync

        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.

        Parameters:
        createEvaluationRequest -
        Returns:
        A Java Future containing the result of the CreateEvaluation operation returned by the service.

      • createEvaluationAsync

        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.

        Parameters:
        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.
        Returns:
        A Java Future containing the result of the CreateEvaluation operation returned by the service.

      • createMLModelAsync

        Future<CreateMLModelResult> createMLModelAsync​(CreateMLModelRequest createMLModelRequest)

        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.

        Parameters:
        createMLModelRequest -
        Returns:
        A Java Future containing the result of the CreateMLModel operation returned by the service.

      • createMLModelAsync

        Future<CreateMLModelResult> createMLModelAsync​(CreateMLModelRequest createMLModelRequest,
                                               AsyncHandler<CreateMLModelRequest,​CreateMLModelResult> asyncHandler)

        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.

        Parameters:
        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.
        Returns:
        A Java Future containing the result of the CreateMLModel operation returned by the service.

      • createRealtimeEndpointAsync

        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 .

        Parameters:
        createRealtimeEndpointRequest -
        Returns:
        A Java Future containing the result of the CreateRealtimeEndpoint operation returned by the service.

      • createRealtimeEndpointAsync

        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 .

        Parameters:
        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.
        Returns:
        A Java Future containing the result of the CreateRealtimeEndpoint operation returned by the service.

      • deleteBatchPredictionAsync

        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.

        Parameters:
        deleteBatchPredictionRequest -
        Returns:
        A Java Future containing the result of the DeleteBatchPrediction operation returned by the service.

      • deleteBatchPredictionAsync

        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.

        Parameters:
        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.
        Returns:
        A Java Future containing the result of the DeleteBatchPrediction operation returned by the service.

      • deleteDataSourceAsync

        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.

        Parameters:
        deleteDataSourceRequest -
        Returns:
        A Java Future containing the result of the DeleteDataSource operation returned by the service.

      • deleteDataSourceAsync

        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.

        Parameters:
        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.
        Returns:
        A Java Future containing the result of the DeleteDataSource operation returned by the service.

      • deleteEvaluationAsync

        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.

        Parameters:
        deleteEvaluationRequest -
        Returns:
        A Java Future containing the result of the DeleteEvaluation operation returned by the service.

      • deleteEvaluationAsync

        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.

        Parameters:
        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.
        Returns:
        A Java Future containing the result of the DeleteEvaluation operation returned by the service.

      • deleteMLModelAsync

        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.

        Parameters:
        deleteMLModelRequest -
        Returns:
        A Java Future containing the result of the DeleteMLModel operation returned by the service.

      • deleteMLModelAsync

        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.

        Parameters:
        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.
        Returns:
        A Java Future containing the result of the DeleteMLModel operation returned by the service.

      • deleteRealtimeEndpointAsync

        Future<DeleteRealtimeEndpointResult> deleteRealtimeEndpointAsync​(DeleteRealtimeEndpointRequest deleteRealtimeEndpointRequest)

        Deletes a real time endpoint of an MLModel.

        Parameters:
        deleteRealtimeEndpointRequest -
        Returns:
        A Java Future containing the result of the DeleteRealtimeEndpoint operation returned by the service.

      • deleteRealtimeEndpointAsync

        Future<DeleteRealtimeEndpointResult> deleteRealtimeEndpointAsync​(DeleteRealtimeEndpointRequest deleteRealtimeEndpointRequest,
                                                                 AsyncHandler<DeleteRealtimeEndpointRequest,​DeleteRealtimeEndpointResult> asyncHandler)

        Deletes a real time endpoint of an MLModel.

        Parameters:
        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.
        Returns:
        A Java Future containing the result of the DeleteRealtimeEndpoint operation returned by the service.

      • describeBatchPredictionsAsync

        Future<DescribeBatchPredictionsResult> describeBatchPredictionsAsync​(DescribeBatchPredictionsRequest describeBatchPredictionsRequest)

        Returns a list of BatchPrediction operations that match the search criteria in the request.

        Parameters:
        describeBatchPredictionsRequest -
        Returns:
        A Java Future containing the result of the DescribeBatchPredictions operation returned by the service.

      • describeBatchPredictionsAsync

        Future<DescribeBatchPredictionsResult> describeBatchPredictionsAsync​(DescribeBatchPredictionsRequest describeBatchPredictionsRequest,
                                                                     AsyncHandler<DescribeBatchPredictionsRequest,​DescribeBatchPredictionsResult> asyncHandler)

        Returns a list of BatchPrediction operations that match the search criteria in the request.

        Parameters:
        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.
        Returns:
        A Java Future containing the result of the DescribeBatchPredictions operation returned by the service.

      • describeDataSourcesAsync

        Future<DescribeDataSourcesResult> describeDataSourcesAsync​(DescribeDataSourcesRequest describeDataSourcesRequest)

        Returns a list of DataSource that match the search criteria in the request.

        Parameters:
        describeDataSourcesRequest -
        Returns:
        A Java Future containing the result of the DescribeDataSources operation returned by the service.

      • describeDataSourcesAsync

        Future<DescribeDataSourcesResult> describeDataSourcesAsync​(DescribeDataSourcesRequest describeDataSourcesRequest,
                                                           AsyncHandler<DescribeDataSourcesRequest,​DescribeDataSourcesResult> asyncHandler)

        Returns a list of DataSource that match the search criteria in the request.

        Parameters:
        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.
        Returns:
        A Java Future containing the result of the DescribeDataSources operation returned by the service.

      • describeEvaluationsAsync

        Future<DescribeEvaluationsResult> describeEvaluationsAsync​(DescribeEvaluationsRequest describeEvaluationsRequest)

        Returns a list of DescribeEvaluations that match the search criteria in the request.

        Parameters:
        describeEvaluationsRequest -
        Returns:
        A Java Future containing the result of the DescribeEvaluations operation returned by the service.

      • describeEvaluationsAsync

        Future<DescribeEvaluationsResult> describeEvaluationsAsync​(DescribeEvaluationsRequest describeEvaluationsRequest,
                                                           AsyncHandler<DescribeEvaluationsRequest,​DescribeEvaluationsResult> asyncHandler)

        Returns a list of DescribeEvaluations that match the search criteria in the request.

        Parameters:
        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.
        Returns:
        A Java Future containing the result of the DescribeEvaluations operation returned by the service.

      • describeMLModelsAsync

        Future<DescribeMLModelsResult> describeMLModelsAsync​(DescribeMLModelsRequest describeMLModelsRequest)

        Returns a list of MLModel that match the search criteria in the request.

        Parameters:
        describeMLModelsRequest -
        Returns:
        A Java Future containing the result of the DescribeMLModels operation returned by the service.

      • describeMLModelsAsync

        Future<DescribeMLModelsResult> describeMLModelsAsync​(DescribeMLModelsRequest describeMLModelsRequest,
                                                     AsyncHandler<DescribeMLModelsRequest,​DescribeMLModelsResult> asyncHandler)

        Returns a list of MLModel that match the search criteria in the request.

        Parameters:
        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.
        Returns:
        A Java Future containing the result of the DescribeMLModels operation returned by the service.

      • getBatchPredictionAsync

        Future<GetBatchPredictionResult> getBatchPredictionAsync​(GetBatchPredictionRequest getBatchPredictionRequest)

        Returns a BatchPrediction that includes detailed metadata, status, and data file information for a Batch Prediction request.

        Parameters:
        getBatchPredictionRequest -
        Returns:
        A Java Future containing the result of the GetBatchPrediction operation returned by the service.

      • getBatchPredictionAsync

        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.

        Parameters:
        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.
        Returns:
        A Java Future containing the result of the GetBatchPrediction operation returned by the service.

      • getDataSourceAsync

        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.

        Parameters:
        getDataSourceRequest -
        Returns:
        A Java Future containing the result of the GetDataSource operation returned by the service.

      • getDataSourceAsync

        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.

        Parameters:
        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.
        Returns:
        A Java Future containing the result of the GetDataSource operation returned by the service.

      • getEvaluationAsync

        Future<GetEvaluationResult> getEvaluationAsync​(GetEvaluationRequest getEvaluationRequest)

        Returns an Evaluation that includes metadata as well as the current status of the Evaluation.

        Parameters:
        getEvaluationRequest -
        Returns:
        A Java Future containing the result of the GetEvaluation operation returned by the service.

      • getEvaluationAsync

        Future<GetEvaluationResult> getEvaluationAsync​(GetEvaluationRequest getEvaluationRequest,
                                               AsyncHandler<GetEvaluationRequest,​GetEvaluationResult> asyncHandler)

        Returns an Evaluation that includes metadata as well as the current status of the Evaluation.

        Parameters:
        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.
        Returns:
        A Java Future containing the result of the GetEvaluation operation returned by the service.

      • getMLModelAsync

        Future<GetMLModelResult> getMLModelAsync​(GetMLModelRequest getMLModelRequest)

        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.

        Parameters:
        getMLModelRequest -
        Returns:
        A Java Future containing the result of the GetMLModel operation returned by the service.

      • getMLModelAsync

        Future<GetMLModelResult> getMLModelAsync​(GetMLModelRequest getMLModelRequest,
                                         AsyncHandler<GetMLModelRequest,​GetMLModelResult> asyncHandler)

        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.

        Parameters:
        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.
        Returns:
        A Java Future containing the result of the GetMLModel operation returned by the service.

      • predictAsync

        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.

        Parameters:
        predictRequest -
        Returns:
        A Java Future containing the result of the Predict operation returned by the service.

      • predictAsync

        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.

        Parameters:
        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.
        Returns:
        A Java Future containing the result of the Predict operation returned by the service.

      • updateBatchPredictionAsync

        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.

        Parameters:
        updateBatchPredictionRequest -
        Returns:
        A Java Future containing the result of the UpdateBatchPrediction operation returned by the service.

      • updateBatchPredictionAsync

        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.

        Parameters:
        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.
        Returns:
        A Java Future containing the result of the UpdateBatchPrediction operation returned by the service.

      • updateDataSourceAsync

        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.

        Parameters:
        updateDataSourceRequest -
        Returns:
        A Java Future containing the result of the UpdateDataSource operation returned by the service.

      • updateDataSourceAsync

        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.

        Parameters:
        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.
        Returns:
        A Java Future containing the result of the UpdateDataSource operation returned by the service.

      • updateEvaluationAsync

        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.

        Parameters:
        updateEvaluationRequest -
        Returns:
        A Java Future containing the result of the UpdateEvaluation operation returned by the service.

      • updateEvaluationAsync

        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.

        Parameters:
        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.
        Returns:
        A Java Future containing the result of the UpdateEvaluation operation returned by the service.

      • updateMLModelAsync

        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.

        Parameters:
        updateMLModelRequest -
        Returns:
        A Java Future containing the result of the UpdateMLModel operation returned by the service.

      • updateMLModelAsync

        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.

        Parameters:
        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.
        Returns:
        A Java Future containing the result of the UpdateMLModel operation returned by the service.