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/*
* Copyright 2010-2016 Amazon.com, Inc. or its affiliates. All Rights
* Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License").
* You may not use this file except in compliance with the License.
* A copy of the License is located at
*
* http://aws.amazon.com/apache2.0
*
* or in the "license" file accompanying this file. This file is distributed
* on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
* express or implied. See the License for the specific language governing
* permissions and limitations under the License.
*/
package com.amazonaws.services.machinelearning;
import com.amazonaws.services.machinelearning.model.*;
/**
* Interface for accessing Amazon Machine Learning asynchronously. Each
* asynchronous method will return a Java Future object representing the
* asynchronous operation; overloads which accept an {@code AsyncHandler} can be
* used to receive notification when an asynchronous operation completes.
*
* Definition of the public APIs exposed by Amazon Machine Learning
*/
public interface AmazonMachineLearningAsync extends AmazonMachineLearning {
/**
*
* 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.
*
*
* @param createBatchPredictionRequest
* @return A Java Future containing the result of the CreateBatchPrediction
* operation returned by the service.
* @sample AmazonMachineLearningAsync.CreateBatchPrediction
*/
java.util.concurrent.Future 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.
*
*
* @param createBatchPredictionRequest
* @param 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.
* @return A Java Future containing the result of the CreateBatchPrediction
* operation returned by the service.
* @sample AmazonMachineLearningAsyncHandler.CreateBatchPrediction
*/
java.util.concurrent.Future createBatchPredictionAsync(
CreateBatchPredictionRequest createBatchPredictionRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
* @param createDataSourceFromRDSRequest
* @return A Java Future containing the result of the
* CreateDataSourceFromRDS operation returned by the service.
* @sample AmazonMachineLearningAsync.CreateDataSourceFromRDS
*/
java.util.concurrent.Future 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.
*
*
* @param createDataSourceFromRDSRequest
* @param 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.
* @return A Java Future containing the result of the
* CreateDataSourceFromRDS operation returned by the service.
* @sample AmazonMachineLearningAsyncHandler.CreateDataSourceFromRDS
*/
java.util.concurrent.Future createDataSourceFromRDSAsync(
CreateDataSourceFromRDSRequest createDataSourceFromRDSRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
* @param createDataSourceFromRedshiftRequest
* @return A Java Future containing the result of the
* CreateDataSourceFromRedshift operation returned by the service.
* @sample AmazonMachineLearningAsync.CreateDataSourceFromRedshift
*/
java.util.concurrent.Future 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.
*
*
* @param createDataSourceFromRedshiftRequest
* @param 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.
* @return A Java Future containing the result of the
* CreateDataSourceFromRedshift operation returned by the service.
* @sample AmazonMachineLearningAsyncHandler.CreateDataSourceFromRedshift
*/
java.util.concurrent.Future createDataSourceFromRedshiftAsync(
CreateDataSourceFromRedshiftRequest createDataSourceFromRedshiftRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
* @param createDataSourceFromS3Request
* @return A Java Future containing the result of the CreateDataSourceFromS3
* operation returned by the service.
* @sample AmazonMachineLearningAsync.CreateDataSourceFromS3
*/
java.util.concurrent.Future 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.
*
*
* @param createDataSourceFromS3Request
* @param 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.
* @return A Java Future containing the result of the CreateDataSourceFromS3
* operation returned by the service.
* @sample AmazonMachineLearningAsyncHandler.CreateDataSourceFromS3
*/
java.util.concurrent.Future createDataSourceFromS3Async(
CreateDataSourceFromS3Request createDataSourceFromS3Request,
com.amazonaws.handlers.AsyncHandler 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.
*
*
* @param createEvaluationRequest
* @return A Java Future containing the result of the CreateEvaluation
* operation returned by the service.
* @sample AmazonMachineLearningAsync.CreateEvaluation
*/
java.util.concurrent.Future 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.
*
*
* @param createEvaluationRequest
* @param 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.
* @return A Java Future containing the result of the CreateEvaluation
* operation returned by the service.
* @sample AmazonMachineLearningAsyncHandler.CreateEvaluation
*/
java.util.concurrent.Future createEvaluationAsync(
CreateEvaluationRequest createEvaluationRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
* @param createMLModelRequest
* @return A Java Future containing the result of the CreateMLModel
* operation returned by the service.
* @sample AmazonMachineLearningAsync.CreateMLModel
*/
java.util.concurrent.Future 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.
*
*
* @param createMLModelRequest
* @param 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.
* @return A Java Future containing the result of the CreateMLModel
* operation returned by the service.
* @sample AmazonMachineLearningAsyncHandler.CreateMLModel
*/
java.util.concurrent.Future createMLModelAsync(
CreateMLModelRequest createMLModelRequest,
com.amazonaws.handlers.AsyncHandler 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
* .
*
*
* @param createRealtimeEndpointRequest
* @return A Java Future containing the result of the CreateRealtimeEndpoint
* operation returned by the service.
* @sample AmazonMachineLearningAsync.CreateRealtimeEndpoint
*/
java.util.concurrent.Future 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
* .
*
*
* @param createRealtimeEndpointRequest
* @param 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.
* @return A Java Future containing the result of the CreateRealtimeEndpoint
* operation returned by the service.
* @sample AmazonMachineLearningAsyncHandler.CreateRealtimeEndpoint
*/
java.util.concurrent.Future createRealtimeEndpointAsync(
CreateRealtimeEndpointRequest createRealtimeEndpointRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
* @param deleteBatchPredictionRequest
* @return A Java Future containing the result of the DeleteBatchPrediction
* operation returned by the service.
* @sample AmazonMachineLearningAsync.DeleteBatchPrediction
*/
java.util.concurrent.Future 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.
*
*
* @param deleteBatchPredictionRequest
* @param 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.
* @return A Java Future containing the result of the DeleteBatchPrediction
* operation returned by the service.
* @sample AmazonMachineLearningAsyncHandler.DeleteBatchPrediction
*/
java.util.concurrent.Future deleteBatchPredictionAsync(
DeleteBatchPredictionRequest deleteBatchPredictionRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
* @param deleteDataSourceRequest
* @return A Java Future containing the result of the DeleteDataSource
* operation returned by the service.
* @sample AmazonMachineLearningAsync.DeleteDataSource
*/
java.util.concurrent.Future 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.
*
*
* @param deleteDataSourceRequest
* @param 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.
* @return A Java Future containing the result of the DeleteDataSource
* operation returned by the service.
* @sample AmazonMachineLearningAsyncHandler.DeleteDataSource
*/
java.util.concurrent.Future deleteDataSourceAsync(
DeleteDataSourceRequest deleteDataSourceRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
* @param deleteEvaluationRequest
* @return A Java Future containing the result of the DeleteEvaluation
* operation returned by the service.
* @sample AmazonMachineLearningAsync.DeleteEvaluation
*/
java.util.concurrent.Future 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.
*
*
* @param deleteEvaluationRequest
* @param 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.
* @return A Java Future containing the result of the DeleteEvaluation
* operation returned by the service.
* @sample AmazonMachineLearningAsyncHandler.DeleteEvaluation
*/
java.util.concurrent.Future deleteEvaluationAsync(
DeleteEvaluationRequest deleteEvaluationRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
* @param deleteMLModelRequest
* @return A Java Future containing the result of the DeleteMLModel
* operation returned by the service.
* @sample AmazonMachineLearningAsync.DeleteMLModel
*/
java.util.concurrent.Future 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.
*
*
* @param deleteMLModelRequest
* @param 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.
* @return A Java Future containing the result of the DeleteMLModel
* operation returned by the service.
* @sample AmazonMachineLearningAsyncHandler.DeleteMLModel
*/
java.util.concurrent.Future deleteMLModelAsync(
DeleteMLModelRequest deleteMLModelRequest,
com.amazonaws.handlers.AsyncHandler asyncHandler);
/**
*
* Deletes a real time endpoint of an MLModel.
*
*
* @param deleteRealtimeEndpointRequest
* @return A Java Future containing the result of the DeleteRealtimeEndpoint
* operation returned by the service.
* @sample AmazonMachineLearningAsync.DeleteRealtimeEndpoint
*/
java.util.concurrent.Future deleteRealtimeEndpointAsync(
DeleteRealtimeEndpointRequest deleteRealtimeEndpointRequest);
/**
*
* Deletes a real time endpoint of an MLModel.
*
*
* @param deleteRealtimeEndpointRequest
* @param 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.
* @return A Java Future containing the result of the DeleteRealtimeEndpoint
* operation returned by the service.
* @sample AmazonMachineLearningAsyncHandler.DeleteRealtimeEndpoint
*/
java.util.concurrent.Future deleteRealtimeEndpointAsync(
DeleteRealtimeEndpointRequest deleteRealtimeEndpointRequest,
com.amazonaws.handlers.AsyncHandler asyncHandler);
/**
*
* Returns a list of BatchPrediction operations that match the
* search criteria in the request.
*
*
* @param describeBatchPredictionsRequest
* @return A Java Future containing the result of the
* DescribeBatchPredictions operation returned by the service.
* @sample AmazonMachineLearningAsync.DescribeBatchPredictions
*/
java.util.concurrent.Future describeBatchPredictionsAsync(
DescribeBatchPredictionsRequest describeBatchPredictionsRequest);
/**
*
* Returns a list of BatchPrediction operations that match the
* search criteria in the request.
*
*
* @param describeBatchPredictionsRequest
* @param 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.
* @return A Java Future containing the result of the
* DescribeBatchPredictions operation returned by the service.
* @sample AmazonMachineLearningAsyncHandler.DescribeBatchPredictions
*/
java.util.concurrent.Future describeBatchPredictionsAsync(
DescribeBatchPredictionsRequest describeBatchPredictionsRequest,
com.amazonaws.handlers.AsyncHandler asyncHandler);
/**
* Simplified method form for invoking the DescribeBatchPredictions
* operation.
*
* @see #describeBatchPredictionsAsync(DescribeBatchPredictionsRequest)
*/
java.util.concurrent.Future describeBatchPredictionsAsync();
/**
* Simplified method form for invoking the DescribeBatchPredictions
* operation with an AsyncHandler.
*
* @see #describeBatchPredictionsAsync(DescribeBatchPredictionsRequest,
* com.amazonaws.handlers.AsyncHandler)
*/
java.util.concurrent.Future describeBatchPredictionsAsync(
com.amazonaws.handlers.AsyncHandler asyncHandler);
/**
*
* Returns a list of DataSource that match the search criteria
* in the request.
*
*
* @param describeDataSourcesRequest
* @return A Java Future containing the result of the DescribeDataSources
* operation returned by the service.
* @sample AmazonMachineLearningAsync.DescribeDataSources
*/
java.util.concurrent.Future describeDataSourcesAsync(
DescribeDataSourcesRequest describeDataSourcesRequest);
/**
*
* Returns a list of DataSource that match the search criteria
* in the request.
*
*
* @param describeDataSourcesRequest
* @param 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.
* @return A Java Future containing the result of the DescribeDataSources
* operation returned by the service.
* @sample AmazonMachineLearningAsyncHandler.DescribeDataSources
*/
java.util.concurrent.Future describeDataSourcesAsync(
DescribeDataSourcesRequest describeDataSourcesRequest,
com.amazonaws.handlers.AsyncHandler asyncHandler);
/**
* Simplified method form for invoking the DescribeDataSources operation.
*
* @see #describeDataSourcesAsync(DescribeDataSourcesRequest)
*/
java.util.concurrent.Future describeDataSourcesAsync();
/**
* Simplified method form for invoking the DescribeDataSources operation
* with an AsyncHandler.
*
* @see #describeDataSourcesAsync(DescribeDataSourcesRequest,
* com.amazonaws.handlers.AsyncHandler)
*/
java.util.concurrent.Future describeDataSourcesAsync(
com.amazonaws.handlers.AsyncHandler asyncHandler);
/**
*
* Returns a list of DescribeEvaluations that match the search
* criteria in the request.
*
*
* @param describeEvaluationsRequest
* @return A Java Future containing the result of the DescribeEvaluations
* operation returned by the service.
* @sample AmazonMachineLearningAsync.DescribeEvaluations
*/
java.util.concurrent.Future describeEvaluationsAsync(
DescribeEvaluationsRequest describeEvaluationsRequest);
/**
*
* Returns a list of DescribeEvaluations that match the search
* criteria in the request.
*
*
* @param describeEvaluationsRequest
* @param 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.
* @return A Java Future containing the result of the DescribeEvaluations
* operation returned by the service.
* @sample AmazonMachineLearningAsyncHandler.DescribeEvaluations
*/
java.util.concurrent.Future describeEvaluationsAsync(
DescribeEvaluationsRequest describeEvaluationsRequest,
com.amazonaws.handlers.AsyncHandler asyncHandler);
/**
* Simplified method form for invoking the DescribeEvaluations operation.
*
* @see #describeEvaluationsAsync(DescribeEvaluationsRequest)
*/
java.util.concurrent.Future describeEvaluationsAsync();
/**
* Simplified method form for invoking the DescribeEvaluations operation
* with an AsyncHandler.
*
* @see #describeEvaluationsAsync(DescribeEvaluationsRequest,
* com.amazonaws.handlers.AsyncHandler)
*/
java.util.concurrent.Future describeEvaluationsAsync(
com.amazonaws.handlers.AsyncHandler asyncHandler);
/**
*
* Returns a list of MLModel that match the search criteria in
* the request.
*
*
* @param describeMLModelsRequest
* @return A Java Future containing the result of the DescribeMLModels
* operation returned by the service.
* @sample AmazonMachineLearningAsync.DescribeMLModels
*/
java.util.concurrent.Future describeMLModelsAsync(
DescribeMLModelsRequest describeMLModelsRequest);
/**
*
* Returns a list of MLModel that match the search criteria in
* the request.
*
*
* @param describeMLModelsRequest
* @param 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.
* @return A Java Future containing the result of the DescribeMLModels
* operation returned by the service.
* @sample AmazonMachineLearningAsyncHandler.DescribeMLModels
*/
java.util.concurrent.Future describeMLModelsAsync(
DescribeMLModelsRequest describeMLModelsRequest,
com.amazonaws.handlers.AsyncHandler asyncHandler);
/**
* Simplified method form for invoking the DescribeMLModels operation.
*
* @see #describeMLModelsAsync(DescribeMLModelsRequest)
*/
java.util.concurrent.Future describeMLModelsAsync();
/**
* Simplified method form for invoking the DescribeMLModels operation with
* an AsyncHandler.
*
* @see #describeMLModelsAsync(DescribeMLModelsRequest,
* com.amazonaws.handlers.AsyncHandler)
*/
java.util.concurrent.Future describeMLModelsAsync(
com.amazonaws.handlers.AsyncHandler asyncHandler);
/**
*
* Returns a BatchPrediction that includes detailed metadata,
* status, and data file information for a Batch Prediction
* request.
*
*
* @param getBatchPredictionRequest
* @return A Java Future containing the result of the GetBatchPrediction
* operation returned by the service.
* @sample AmazonMachineLearningAsync.GetBatchPrediction
*/
java.util.concurrent.Future getBatchPredictionAsync(
GetBatchPredictionRequest getBatchPredictionRequest);
/**
*
* Returns a BatchPrediction that includes detailed metadata,
* status, and data file information for a Batch Prediction
* request.
*
*
* @param getBatchPredictionRequest
* @param 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.
* @return A Java Future containing the result of the GetBatchPrediction
* operation returned by the service.
* @sample AmazonMachineLearningAsyncHandler.GetBatchPrediction
*/
java.util.concurrent.Future getBatchPredictionAsync(
GetBatchPredictionRequest getBatchPredictionRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
* @param getDataSourceRequest
* @return A Java Future containing the result of the GetDataSource
* operation returned by the service.
* @sample AmazonMachineLearningAsync.GetDataSource
*/
java.util.concurrent.Future 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.
*
*
* @param getDataSourceRequest
* @param 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.
* @return A Java Future containing the result of the GetDataSource
* operation returned by the service.
* @sample AmazonMachineLearningAsyncHandler.GetDataSource
*/
java.util.concurrent.Future getDataSourceAsync(
GetDataSourceRequest getDataSourceRequest,
com.amazonaws.handlers.AsyncHandler asyncHandler);
/**
*
* Returns an Evaluation that includes metadata as well as the
* current status of the Evaluation.
*
*
* @param getEvaluationRequest
* @return A Java Future containing the result of the GetEvaluation
* operation returned by the service.
* @sample AmazonMachineLearningAsync.GetEvaluation
*/
java.util.concurrent.Future getEvaluationAsync(
GetEvaluationRequest getEvaluationRequest);
/**
*
* Returns an Evaluation that includes metadata as well as the
* current status of the Evaluation.
*
*
* @param getEvaluationRequest
* @param 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.
* @return A Java Future containing the result of the GetEvaluation
* operation returned by the service.
* @sample AmazonMachineLearningAsyncHandler.GetEvaluation
*/
java.util.concurrent.Future getEvaluationAsync(
GetEvaluationRequest getEvaluationRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
* @param getMLModelRequest
* @return A Java Future containing the result of the GetMLModel operation
* returned by the service.
* @sample AmazonMachineLearningAsync.GetMLModel
*/
java.util.concurrent.Future 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.
*
*
* @param getMLModelRequest
* @param 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.
* @return A Java Future containing the result of the GetMLModel operation
* returned by the service.
* @sample AmazonMachineLearningAsyncHandler.GetMLModel
*/
java.util.concurrent.Future getMLModelAsync(
GetMLModelRequest getMLModelRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
* 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.
*
* predictAsync(
PredictRequest predictRequest,
com.amazonaws.handlers.AsyncHandler asyncHandler);
/**
*
* Updates the BatchPredictionName of a
* BatchPrediction.
*
*
* You can use the GetBatchPrediction operation to view the contents
* of the updated data element.
*
*
* @param updateBatchPredictionRequest
* @return A Java Future containing the result of the UpdateBatchPrediction
* operation returned by the service.
* @sample AmazonMachineLearningAsync.UpdateBatchPrediction
*/
java.util.concurrent.Future updateBatchPredictionAsync(
UpdateBatchPredictionRequest updateBatchPredictionRequest);
/**
*
* Updates the BatchPredictionName of a
* BatchPrediction.
*
*
* You can use the GetBatchPrediction operation to view the contents
* of the updated data element.
*
*
* @param updateBatchPredictionRequest
* @param 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.
* @return A Java Future containing the result of the UpdateBatchPrediction
* operation returned by the service.
* @sample AmazonMachineLearningAsyncHandler.UpdateBatchPrediction
*/
java.util.concurrent.Future updateBatchPredictionAsync(
UpdateBatchPredictionRequest updateBatchPredictionRequest,
com.amazonaws.handlers.AsyncHandler asyncHandler);
/**
*
* Updates the DataSourceName of a DataSource.
*
*
* You can use the GetDataSource operation to view the contents of
* the updated data element.
*
*
* @param updateDataSourceRequest
* @return A Java Future containing the result of the UpdateDataSource
* operation returned by the service.
* @sample AmazonMachineLearningAsync.UpdateDataSource
*/
java.util.concurrent.Future updateDataSourceAsync(
UpdateDataSourceRequest updateDataSourceRequest);
/**
*
* Updates the DataSourceName of a DataSource.
*
*
* You can use the GetDataSource operation to view the contents of
* the updated data element.
*
*
* @param updateDataSourceRequest
* @param 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.
* @return A Java Future containing the result of the UpdateDataSource
* operation returned by the service.
* @sample AmazonMachineLearningAsyncHandler.UpdateDataSource
*/
java.util.concurrent.Future updateDataSourceAsync(
UpdateDataSourceRequest updateDataSourceRequest,
com.amazonaws.handlers.AsyncHandler asyncHandler);
/**
*
* Updates the EvaluationName of an Evaluation.
*
*
* You can use the GetEvaluation operation to view the contents of
* the updated data element.
*
*
* @param updateEvaluationRequest
* @return A Java Future containing the result of the UpdateEvaluation
* operation returned by the service.
* @sample AmazonMachineLearningAsync.UpdateEvaluation
*/
java.util.concurrent.Future updateEvaluationAsync(
UpdateEvaluationRequest updateEvaluationRequest);
/**
*
* Updates the EvaluationName of an Evaluation.
*
*
* You can use the GetEvaluation operation to view the contents of
* the updated data element.
*
*
* @param updateEvaluationRequest
* @param 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.
* @return A Java Future containing the result of the UpdateEvaluation
* operation returned by the service.
* @sample AmazonMachineLearningAsyncHandler.UpdateEvaluation
*/
java.util.concurrent.Future updateEvaluationAsync(
UpdateEvaluationRequest updateEvaluationRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
* @param updateMLModelRequest
* @return A Java Future containing the result of the UpdateMLModel
* operation returned by the service.
* @sample AmazonMachineLearningAsync.UpdateMLModel
*/
java.util.concurrent.Future 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.
*
*
* @param updateMLModelRequest
* @param 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.
* @return A Java Future containing the result of the UpdateMLModel
* operation returned by the service.
* @sample AmazonMachineLearningAsyncHandler.UpdateMLModel
*/
java.util.concurrent.Future updateMLModelAsync(
UpdateMLModelRequest updateMLModelRequest,
com.amazonaws.handlers.AsyncHandler asyncHandler);
}