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/*
* Copyright 2019-2024 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 javax.annotation.Generated;
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.
*
* Note: Do not directly implement this interface, new methods are added to it regularly. Extend from
* {@link com.amazonaws.services.machinelearning.AbstractAmazonMachineLearningAsync} instead.
*
*
* Definition of the public APIs exposed by Amazon Machine Learning
*/
@Generated("com.amazonaws:aws-java-sdk-code-generator")
public interface AmazonMachineLearningAsync extends AmazonMachineLearning {
/**
*
* Adds one or more tags to an object, up to a limit of 10. Each tag consists of a key and an optional value. If you
* add a tag using a key that is already associated with the ML object, AddTags updates the tag's
* value.
*
*
* @param addTagsRequest
* @return A Java Future containing the result of the AddTags operation returned by the service.
* @sample AmazonMachineLearningAsync.AddTags
*/
java.util.concurrent.Future addTagsAsync(AddTagsRequest addTagsRequest);
/**
*
* Adds one or more tags to an object, up to a limit of 10. Each tag consists of a key and an optional value. If you
* add a tag using a key that is already associated with the ML object, AddTags updates the tag's
* value.
*
*
* @param addTagsRequest
* @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 AddTags operation returned by the service.
* @sample AmazonMachineLearningAsyncHandler.AddTags
*/
java.util.concurrent.Future addTagsAsync(AddTagsRequest addTagsRequest,
com.amazonaws.handlers.AsyncHandler 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.
*
*
* @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
* the COMPLETED or PENDING state can be used only to perform
* >CreateMLModel>, CreateEvaluation, or CreateBatchPrediction
* operations.
*
*
* If Amazon ML cannot accept the input source, it sets the Status parameter to FAILED and
* includes an error message in the Message attribute of the GetDataSource operation
* response.
*
*
* @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
* the COMPLETED or PENDING state can be used only to perform
* >CreateMLModel>, CreateEvaluation, or CreateBatchPrediction
* operations.
*
*
* If Amazon ML cannot accept the input source, it sets the Status parameter to FAILED and
* includes an error message in the Message attribute of the GetDataSource operation
* response.
*
*
* @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 a database hosted on an Amazon Redshift cluster. A DataSource
* references data that can be used to perform either CreateMLModel, CreateEvaluation, or
* CreateBatchPrediction operations.
*
*
* CreateDataSourceFromRedshift is an asynchronous operation. In response to
* CreateDataSourceFromRedshift, Amazon Machine Learning (Amazon ML) immediately returns and sets the
* DataSource status to PENDING. After the DataSource is created and ready
* for use, Amazon ML sets the Status parameter to COMPLETED. DataSource in
* COMPLETED or PENDING states can be used to perform only CreateMLModel,
* CreateEvaluation, or CreateBatchPrediction operations.
*
*
* If Amazon ML can't accept the input source, it sets the Status parameter to FAILED and
* includes an error message in the Message attribute of the GetDataSource operation
* response.
*
*
* The observations should be contained in the database hosted on an Amazon Redshift cluster and should be specified
* by a SelectSqlQuery query. Amazon ML executes an Unload command in Amazon Redshift to
* transfer the result set of the SelectSqlQuery query to S3StagingLocation.
*
*
* After the DataSource has been created, it's ready for use in evaluations and batch predictions. If
* you plan to use the DataSource to train an MLModel, the DataSource also
* requires a recipe. A recipe describes how each input variable will be used in training an MLModel.
* Will the variable be included or excluded from training? Will the variable be manipulated; for example, will it
* be combined with another variable or will it be split apart into word combinations? The recipe provides answers
* to these questions.
*
*
* You can't change an existing datasource, but you can copy and modify the settings from an existing Amazon
* Redshift datasource to create a new datasource. To do so, call GetDataSource for an existing
* datasource and copy the values to a CreateDataSource call. Change the settings that you want to
* change and make sure that all required fields have the appropriate values.
*
*
* @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 a database hosted on an Amazon Redshift cluster. A DataSource
* references data that can be used to perform either CreateMLModel, CreateEvaluation, or
* CreateBatchPrediction operations.
*
*
* CreateDataSourceFromRedshift is an asynchronous operation. In response to
* CreateDataSourceFromRedshift, Amazon Machine Learning (Amazon ML) immediately returns and sets the
* DataSource status to PENDING. After the DataSource is created and ready
* for use, Amazon ML sets the Status parameter to COMPLETED. DataSource in
* COMPLETED or PENDING states can be used to perform only CreateMLModel,
* CreateEvaluation, or CreateBatchPrediction operations.
*
*
* If Amazon ML can't accept the input source, it sets the Status parameter to FAILED and
* includes an error message in the Message attribute of the GetDataSource operation
* response.
*
*
* The observations should be contained in the database hosted on an Amazon Redshift cluster and should be specified
* by a SelectSqlQuery query. Amazon ML executes an Unload command in Amazon Redshift to
* transfer the result set of the SelectSqlQuery query to S3StagingLocation.
*
*
* After the DataSource has been created, it's ready for use in evaluations and batch predictions. If
* you plan to use the DataSource to train an MLModel, the DataSource also
* requires a recipe. A recipe describes how each input variable will be used in training an MLModel.
* Will the variable be included or excluded from training? Will the variable be manipulated; for example, will it
* be combined with another variable or will it be split apart into word combinations? The recipe provides answers
* to these questions.
*
*
* You can't change an existing datasource, but you can copy and modify the settings from an existing Amazon
* Redshift datasource to create a new datasource. To do so, call GetDataSource for an existing
* datasource and copy the values to a CreateDataSource call. Change the settings that you want to
* change and make sure that all required fields have the appropriate values.
*
*
* @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 has been created and is
* ready for use, Amazon ML sets the Status parameter to COMPLETED.
* DataSource in the COMPLETED or PENDING state can be used to perform only
* CreateMLModel, CreateEvaluation or CreateBatchPrediction operations.
*
*
* If Amazon ML can't accept the input source, it sets the Status parameter to FAILED and
* includes an error message in the Message attribute of the GetDataSource operation
* response.
*
*
* The observation data used in a DataSource should be ready to use; that is, it should have a
* consistent structure, and missing data values should be kept to a minimum. The observation data must reside in
* one or more .csv files in an Amazon Simple Storage Service (Amazon S3) location, along with a schema that
* describes the data items by name and type. The same schema must be used for all of the data files referenced by
* the DataSource.
*
*
* After the DataSource has been created, it's ready to use in evaluations and batch predictions. If
* you plan to use the DataSource to train an MLModel, the DataSource also
* needs a recipe. A recipe describes how each input variable will be used in training an MLModel. Will
* the variable be included or excluded from training? Will the variable be manipulated; for example, will it be
* combined with another variable or will it be split apart into word combinations? The recipe provides answers to
* these questions.
*
*
* @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 has been created and is
* ready for use, Amazon ML sets the Status parameter to COMPLETED.
* DataSource in the COMPLETED or PENDING state can be used to perform only
* CreateMLModel, CreateEvaluation or CreateBatchPrediction operations.
*
*
* If Amazon ML can't accept the input source, it sets the Status parameter to FAILED and
* includes an error message in the Message attribute of the GetDataSource operation
* response.
*
*
* The observation data used in a DataSource should be ready to use; that is, it should have a
* consistent structure, and missing data values should be kept to a minimum. The observation data must reside in
* one or more .csv files in an Amazon Simple Storage Service (Amazon S3) location, along with a schema that
* describes the data items by name and type. The same schema must be used for all of the data files referenced by
* the DataSource.
*
*
* After the DataSource has been created, it's ready to use in evaluations and batch predictions. If
* you plan to use the DataSource to train an MLModel, the DataSource also
* needs a recipe. A recipe describes how each input variable will be used in training an MLModel. Will
* the variable be included or excluded from training? Will the variable be manipulated; for example, will it be
* combined with another variable or will it be split apart into word combinations? The recipe provides answers to
* these questions.
*
*
* @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 DataSource and the recipe as information sources.
*
*
* An MLModel is nearly immutable. Users can update only the MLModelName and the
* ScoreThreshold in an MLModel without creating a new MLModel.
*
*
* CreateMLModel is an asynchronous operation. In response to CreateMLModel, Amazon
* Machine Learning (Amazon ML) immediately returns and sets the MLModel status to PENDING
* . After the MLModel has been created and ready is for use, Amazon ML sets the status to
* COMPLETED.
*
*
* You can use the GetMLModel operation to check the progress of the MLModel during the
* creation operation.
*
*
* CreateMLModel requires a DataSource with computed statistics, which can be created by
* setting ComputeStatistics to true in CreateDataSourceFromRDS,
* CreateDataSourceFromS3, or CreateDataSourceFromRedshift operations.
*
*
* @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 DataSource and the recipe as information sources.
*
*
* An MLModel is nearly immutable. Users can update only the MLModelName and the
* ScoreThreshold in an MLModel without creating a new MLModel.
*
*
* CreateMLModel is an asynchronous operation. In response to CreateMLModel, Amazon
* Machine Learning (Amazon ML) immediately returns and sets the MLModel status to PENDING
* . After the MLModel has been created and ready is for use, Amazon ML sets the status to
* COMPLETED.
*
*
* You can use the GetMLModel operation to check the progress of the MLModel during the
* creation operation.
*
*
* CreateMLModel requires a DataSource with computed statistics, which can be created by
* setting ComputeStatistics to true in CreateDataSourceFromRDS,
* CreateDataSourceFromS3, or CreateDataSourceFromRedshift operations.
*
*
* @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);
/**
*
* Deletes the specified tags associated with an ML object. After this operation is complete, you can't recover
* deleted tags.
*
*
* If you specify a tag that doesn't exist, Amazon ML ignores it.
*
*
* @param deleteTagsRequest
* @return A Java Future containing the result of the DeleteTags operation returned by the service.
* @sample AmazonMachineLearningAsync.DeleteTags
*/
java.util.concurrent.Future deleteTagsAsync(DeleteTagsRequest deleteTagsRequest);
/**
*
* Deletes the specified tags associated with an ML object. After this operation is complete, you can't recover
* deleted tags.
*
*
* If you specify a tag that doesn't exist, Amazon ML ignores it.
*
*
* @param deleteTagsRequest
* @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 DeleteTags operation returned by the service.
* @sample AmazonMachineLearningAsyncHandler.DeleteTags
*/
java.util.concurrent.Future deleteTagsAsync(DeleteTagsRequest deleteTagsRequest,
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);
/**
*
* Describes one or more of the tags for your Amazon ML object.
*
*
* @param describeTagsRequest
* @return A Java Future containing the result of the DescribeTags operation returned by the service.
* @sample AmazonMachineLearningAsync.DescribeTags
*/
java.util.concurrent.Future describeTagsAsync(DescribeTagsRequest describeTagsRequest);
/**
*
* Describes one or more of the tags for your Amazon ML object.
*
*
* @param describeTagsRequest
* @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 DescribeTags operation returned by the service.
* @sample AmazonMachineLearningAsyncHandler.DescribeTags
*/
java.util.concurrent.Future describeTagsAsync(DescribeTagsRequest describeTagsRequest,
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, data source information, and 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, data source information, and 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.
*
*
* @param predictRequest
* @return A Java Future containing the result of the Predict operation returned by the service.
* @sample AmazonMachineLearningAsync.Predict
*/
java.util.concurrent.Future 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.
*
*
* @param predictRequest
* @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 Predict operation returned by the service.
* @sample AmazonMachineLearningAsyncHandler.Predict
*/
java.util.concurrent.Future 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);
}