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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.*;
import com.amazonaws.regions.*;
import com.amazonaws.services.machinelearning.model.*;
/**
* Interface for accessing Amazon Machine Learning.
*
* Definition of the public APIs exposed by Amazon Machine Learning
*/
public interface AmazonMachineLearning {
/**
* Overrides the default endpoint for this client
* ("https://machinelearning.us-east-1.amazonaws.com"). Callers can use this
* method to control which AWS region they want to work with.
*
* Callers can pass in just the endpoint (ex:
* "machinelearning.us-east-1.amazonaws.com") or a full URL, including the
* protocol (ex: "https://machinelearning.us-east-1.amazonaws.com"). If the
* protocol is not specified here, the default protocol from this client's
* {@link ClientConfiguration} will be used, which by default is HTTPS.
*
* For more information on using AWS regions with the AWS SDK for Java, and
* a complete list of all available endpoints for all AWS services, see: http://developer.amazonwebservices.com/connect/entry.jspa?externalID=
* 3912
*
* This method is not threadsafe. An endpoint should be configured when
* the client is created and before any service requests are made. Changing
* it afterwards creates inevitable race conditions for any service requests
* in transit or retrying.
*
* @param endpoint
* The endpoint (ex: "machinelearning.us-east-1.amazonaws.com") or a
* full URL, including the protocol (ex:
* "https://machinelearning.us-east-1.amazonaws.com") of the region
* specific AWS endpoint this client will communicate with.
*/
void setEndpoint(String endpoint);
/**
* An alternative to {@link AmazonMachineLearning#setEndpoint(String)}, sets
* the regional endpoint for this client's service calls. Callers can use
* this method to control which AWS region they want to work with.
*
* By default, all service endpoints in all regions use the https protocol.
* To use http instead, specify it in the {@link ClientConfiguration}
* supplied at construction.
*
* This method is not threadsafe. A region should be configured when the
* client is created and before any service requests are made. Changing it
* afterwards creates inevitable race conditions for any service requests in
* transit or retrying.
*
* @param region
* The region this client will communicate with. See
* {@link Region#getRegion(com.amazonaws.regions.Regions)} for
* accessing a given region. Must not be null and must be a region
* where the service is available.
*
* @see Region#getRegion(com.amazonaws.regions.Regions)
* @see Region#createClient(Class,
* com.amazonaws.auth.AWSCredentialsProvider, ClientConfiguration)
* @see Region#isServiceSupported(String)
*/
void setRegion(Region region);
/**
*
* 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 Result of the CreateBatchPrediction operation returned by the
* service.
* @throws InvalidInputException
* An error on the client occurred. Typically, the cause is an
* invalid input value.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @throws IdempotentParameterMismatchException
* A second request to use or change an object was not allowed. This
* can result from retrying a request using a parameter that was not
* present in the original request.
* @sample AmazonMachineLearning.CreateBatchPrediction
*/
CreateBatchPredictionResult createBatchPrediction(
CreateBatchPredictionRequest createBatchPredictionRequest);
/**
*
* 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 Result of the CreateDataSourceFromRDS operation returned by the
* service.
* @throws InvalidInputException
* An error on the client occurred. Typically, the cause is an
* invalid input value.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @throws IdempotentParameterMismatchException
* A second request to use or change an object was not allowed. This
* can result from retrying a request using a parameter that was not
* present in the original request.
* @sample AmazonMachineLearning.CreateDataSourceFromRDS
*/
CreateDataSourceFromRDSResult createDataSourceFromRDS(
CreateDataSourceFromRDSRequest createDataSourceFromRDSRequest);
/**
*
* 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 Result of the CreateDataSourceFromRedshift operation returned by
* the service.
* @throws InvalidInputException
* An error on the client occurred. Typically, the cause is an
* invalid input value.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @throws IdempotentParameterMismatchException
* A second request to use or change an object was not allowed. This
* can result from retrying a request using a parameter that was not
* present in the original request.
* @sample AmazonMachineLearning.CreateDataSourceFromRedshift
*/
CreateDataSourceFromRedshiftResult createDataSourceFromRedshift(
CreateDataSourceFromRedshiftRequest createDataSourceFromRedshiftRequest);
/**
*
* 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 Result of the CreateDataSourceFromS3 operation returned by the
* service.
* @throws InvalidInputException
* An error on the client occurred. Typically, the cause is an
* invalid input value.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @throws IdempotentParameterMismatchException
* A second request to use or change an object was not allowed. This
* can result from retrying a request using a parameter that was not
* present in the original request.
* @sample AmazonMachineLearning.CreateDataSourceFromS3
*/
CreateDataSourceFromS3Result createDataSourceFromS3(
CreateDataSourceFromS3Request createDataSourceFromS3Request);
/**
*
* 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 Result of the CreateEvaluation operation returned by the service.
* @throws InvalidInputException
* An error on the client occurred. Typically, the cause is an
* invalid input value.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @throws IdempotentParameterMismatchException
* A second request to use or change an object was not allowed. This
* can result from retrying a request using a parameter that was not
* present in the original request.
* @sample AmazonMachineLearning.CreateEvaluation
*/
CreateEvaluationResult createEvaluation(
CreateEvaluationRequest createEvaluationRequest);
/**
*
* 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 Result of the CreateMLModel operation returned by the service.
* @throws InvalidInputException
* An error on the client occurred. Typically, the cause is an
* invalid input value.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @throws IdempotentParameterMismatchException
* A second request to use or change an object was not allowed. This
* can result from retrying a request using a parameter that was not
* present in the original request.
* @sample AmazonMachineLearning.CreateMLModel
*/
CreateMLModelResult createMLModel(CreateMLModelRequest createMLModelRequest);
/**
*
* 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 Result of the CreateRealtimeEndpoint operation returned by the
* service.
* @throws InvalidInputException
* An error on the client occurred. Typically, the cause is an
* invalid input value.
* @throws ResourceNotFoundException
* A specified resource cannot be located.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @sample AmazonMachineLearning.CreateRealtimeEndpoint
*/
CreateRealtimeEndpointResult createRealtimeEndpoint(
CreateRealtimeEndpointRequest createRealtimeEndpointRequest);
/**
*
* 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 Result of the DeleteBatchPrediction operation returned by the
* service.
* @throws InvalidInputException
* An error on the client occurred. Typically, the cause is an
* invalid input value.
* @throws ResourceNotFoundException
* A specified resource cannot be located.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @sample AmazonMachineLearning.DeleteBatchPrediction
*/
DeleteBatchPredictionResult deleteBatchPrediction(
DeleteBatchPredictionRequest deleteBatchPredictionRequest);
/**
*
* 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 Result of the DeleteDataSource operation returned by the service.
* @throws InvalidInputException
* An error on the client occurred. Typically, the cause is an
* invalid input value.
* @throws ResourceNotFoundException
* A specified resource cannot be located.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @sample AmazonMachineLearning.DeleteDataSource
*/
DeleteDataSourceResult deleteDataSource(
DeleteDataSourceRequest deleteDataSourceRequest);
/**
*
* 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 Result of the DeleteEvaluation operation returned by the service.
* @throws InvalidInputException
* An error on the client occurred. Typically, the cause is an
* invalid input value.
* @throws ResourceNotFoundException
* A specified resource cannot be located.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @sample AmazonMachineLearning.DeleteEvaluation
*/
DeleteEvaluationResult deleteEvaluation(
DeleteEvaluationRequest deleteEvaluationRequest);
/**
*
* 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 Result of the DeleteMLModel operation returned by the service.
* @throws InvalidInputException
* An error on the client occurred. Typically, the cause is an
* invalid input value.
* @throws ResourceNotFoundException
* A specified resource cannot be located.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @sample AmazonMachineLearning.DeleteMLModel
*/
DeleteMLModelResult deleteMLModel(DeleteMLModelRequest deleteMLModelRequest);
/**
*
* Deletes a real time endpoint of an MLModel.
*
*
* @param deleteRealtimeEndpointRequest
* @return Result of the DeleteRealtimeEndpoint operation returned by the
* service.
* @throws InvalidInputException
* An error on the client occurred. Typically, the cause is an
* invalid input value.
* @throws ResourceNotFoundException
* A specified resource cannot be located.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @sample AmazonMachineLearning.DeleteRealtimeEndpoint
*/
DeleteRealtimeEndpointResult deleteRealtimeEndpoint(
DeleteRealtimeEndpointRequest deleteRealtimeEndpointRequest);
/**
*
* Returns a list of BatchPrediction operations that match the
* search criteria in the request.
*
*
* @param describeBatchPredictionsRequest
* @return Result of the DescribeBatchPredictions operation returned by the
* service.
* @throws InvalidInputException
* An error on the client occurred. Typically, the cause is an
* invalid input value.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @sample AmazonMachineLearning.DescribeBatchPredictions
*/
DescribeBatchPredictionsResult describeBatchPredictions(
DescribeBatchPredictionsRequest describeBatchPredictionsRequest);
/**
* Simplified method form for invoking the DescribeBatchPredictions
* operation.
*
* @see #describeBatchPredictions(DescribeBatchPredictionsRequest)
*/
DescribeBatchPredictionsResult describeBatchPredictions();
/**
*
* Returns a list of DataSource that match the search criteria
* in the request.
*
*
* @param describeDataSourcesRequest
* @return Result of the DescribeDataSources operation returned by the
* service.
* @throws InvalidInputException
* An error on the client occurred. Typically, the cause is an
* invalid input value.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @sample AmazonMachineLearning.DescribeDataSources
*/
DescribeDataSourcesResult describeDataSources(
DescribeDataSourcesRequest describeDataSourcesRequest);
/**
* Simplified method form for invoking the DescribeDataSources operation.
*
* @see #describeDataSources(DescribeDataSourcesRequest)
*/
DescribeDataSourcesResult describeDataSources();
/**
*
* Returns a list of DescribeEvaluations that match the search
* criteria in the request.
*
*
* @param describeEvaluationsRequest
* @return Result of the DescribeEvaluations operation returned by the
* service.
* @throws InvalidInputException
* An error on the client occurred. Typically, the cause is an
* invalid input value.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @sample AmazonMachineLearning.DescribeEvaluations
*/
DescribeEvaluationsResult describeEvaluations(
DescribeEvaluationsRequest describeEvaluationsRequest);
/**
* Simplified method form for invoking the DescribeEvaluations operation.
*
* @see #describeEvaluations(DescribeEvaluationsRequest)
*/
DescribeEvaluationsResult describeEvaluations();
/**
*
* Returns a list of MLModel that match the search criteria in
* the request.
*
*
* @param describeMLModelsRequest
* @return Result of the DescribeMLModels operation returned by the service.
* @throws InvalidInputException
* An error on the client occurred. Typically, the cause is an
* invalid input value.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @sample AmazonMachineLearning.DescribeMLModels
*/
DescribeMLModelsResult describeMLModels(
DescribeMLModelsRequest describeMLModelsRequest);
/**
* Simplified method form for invoking the DescribeMLModels operation.
*
* @see #describeMLModels(DescribeMLModelsRequest)
*/
DescribeMLModelsResult describeMLModels();
/**
*
* Returns a BatchPrediction that includes detailed metadata,
* status, and data file information for a Batch Prediction
* request.
*
*
* @param getBatchPredictionRequest
* @return Result of the GetBatchPrediction operation returned by the
* service.
* @throws InvalidInputException
* An error on the client occurred. Typically, the cause is an
* invalid input value.
* @throws ResourceNotFoundException
* A specified resource cannot be located.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @sample AmazonMachineLearning.GetBatchPrediction
*/
GetBatchPredictionResult getBatchPrediction(
GetBatchPredictionRequest getBatchPredictionRequest);
/**
*
* 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 Result of the GetDataSource operation returned by the service.
* @throws InvalidInputException
* An error on the client occurred. Typically, the cause is an
* invalid input value.
* @throws ResourceNotFoundException
* A specified resource cannot be located.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @sample AmazonMachineLearning.GetDataSource
*/
GetDataSourceResult getDataSource(GetDataSourceRequest getDataSourceRequest);
/**
*
* Returns an Evaluation that includes metadata as well as the
* current status of the Evaluation.
*
*
* @param getEvaluationRequest
* @return Result of the GetEvaluation operation returned by the service.
* @throws InvalidInputException
* An error on the client occurred. Typically, the cause is an
* invalid input value.
* @throws ResourceNotFoundException
* A specified resource cannot be located.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @sample AmazonMachineLearning.GetEvaluation
*/
GetEvaluationResult getEvaluation(GetEvaluationRequest getEvaluationRequest);
/**
*
* 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 Result of the GetMLModel operation returned by the service.
* @throws InvalidInputException
* An error on the client occurred. Typically, the cause is an
* invalid input value.
* @throws ResourceNotFoundException
* A specified resource cannot be located.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @sample AmazonMachineLearning.GetMLModel
*/
GetMLModelResult getMLModel(GetMLModelRequest getMLModelRequest);
/**
*
* 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.
*
* DataSource.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @throws PredictorNotMountedException
* The exception is thrown when a predict request is made to an
* unmounted MLModel.
* @sample AmazonMachineLearning.Predict
*/
PredictResult predict(PredictRequest predictRequest);
/**
*
* Updates the BatchPredictionName of a
* BatchPrediction.
*
*
* You can use the GetBatchPrediction operation to view the contents
* of the updated data element.
*
*
* @param updateBatchPredictionRequest
* @return Result of the UpdateBatchPrediction operation returned by the
* service.
* @throws InvalidInputException
* An error on the client occurred. Typically, the cause is an
* invalid input value.
* @throws ResourceNotFoundException
* A specified resource cannot be located.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @sample AmazonMachineLearning.UpdateBatchPrediction
*/
UpdateBatchPredictionResult updateBatchPrediction(
UpdateBatchPredictionRequest updateBatchPredictionRequest);
/**
*
* Updates the DataSourceName of a DataSource.
*
*
* You can use the GetDataSource operation to view the contents of
* the updated data element.
*
*
* @param updateDataSourceRequest
* @return Result of the UpdateDataSource operation returned by the service.
* @throws InvalidInputException
* An error on the client occurred. Typically, the cause is an
* invalid input value.
* @throws ResourceNotFoundException
* A specified resource cannot be located.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @sample AmazonMachineLearning.UpdateDataSource
*/
UpdateDataSourceResult updateDataSource(
UpdateDataSourceRequest updateDataSourceRequest);
/**
*
* Updates the EvaluationName of an Evaluation.
*
*
* You can use the GetEvaluation operation to view the contents of
* the updated data element.
*
*
* @param updateEvaluationRequest
* @return Result of the UpdateEvaluation operation returned by the service.
* @throws InvalidInputException
* An error on the client occurred. Typically, the cause is an
* invalid input value.
* @throws ResourceNotFoundException
* A specified resource cannot be located.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @sample AmazonMachineLearning.UpdateEvaluation
*/
UpdateEvaluationResult updateEvaluation(
UpdateEvaluationRequest updateEvaluationRequest);
/**
*
* 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 Result of the UpdateMLModel operation returned by the service.
* @throws InvalidInputException
* An error on the client occurred. Typically, the cause is an
* invalid input value.
* @throws ResourceNotFoundException
* A specified resource cannot be located.
* @throws InternalServerException
* An error on the server occurred when trying to process a request.
* @sample AmazonMachineLearning.UpdateMLModel
*/
UpdateMLModelResult updateMLModel(UpdateMLModelRequest updateMLModelRequest);
/**
* Shuts down this client object, releasing any resources that might be held
* open. This is an optional method, and callers are not expected to call
* it, but can if they want to explicitly release any open resources. Once a
* client has been shutdown, it should not be used to make any more
* requests.
*/
void shutdown();
/**
* Returns additional metadata for a previously executed successful request,
* typically used for debugging issues where a service isn't acting as
* expected. This data isn't considered part of the result data returned by
* an operation, so it's available through this separate, diagnostic
* interface.
*
* Response metadata is only cached for a limited period of time, so if you
* need to access this extra diagnostic information for an executed request,
* you should use this method to retrieve it as soon as possible after
* executing a request.
*
* @param request
* The originally executed request.
*
* @return The response metadata for the specified request, or null if none
* is available.
*/
ResponseMetadata getCachedResponseMetadata(AmazonWebServiceRequest request);
}