AmazonMachineLearning (AWS SDK for Java

All Known Subinterfaces:

AmazonMachineLearningAsync

All Known Implementing Classes:

AbstractAmazonMachineLearning, AbstractAmazonMachineLearningAsync, AmazonMachineLearningAsyncClient, AmazonMachineLearningClient
```
public interface AmazonMachineLearning
```
Interface for accessing Amazon Machine Learning.
Definition of the public APIs exposed by Amazon Machine Learning

Field Summary

Fields
Modifier and Type Field and Description

static String ENDPOINT_PREFIX
The region metadata service name for computing region endpoints.

Fields
Modifier and Type	Field and Description
`static String`	`ENDPOINT_PREFIX` The region metadata service name for computing region endpoints.

Method Summary

All Methods Instance Methods Abstract Methods
Modifier and Type	Method and Description
`AddTagsResult`	`addTags(AddTagsRequest addTagsRequest)` Adds one or more tags to an object, up to a limit of 10.
`CreateBatchPredictionResult`	`createBatchPrediction(CreateBatchPredictionRequest createBatchPredictionRequest)` Generates predictions for a group of observations.
`CreateDataSourceFromRDSResult`	`createDataSourceFromRDS(CreateDataSourceFromRDSRequest createDataSourceFromRDSRequest)` Creates a `DataSource` object from an Amazon Relational Database Service (Amazon RDS).
`CreateDataSourceFromRedshiftResult`	`createDataSourceFromRedshift(CreateDataSourceFromRedshiftRequest createDataSourceFromRedshiftRequest)` Creates a `DataSource` from a database hosted on an Amazon Redshift cluster.
`CreateDataSourceFromS3Result`	`createDataSourceFromS3(CreateDataSourceFromS3Request createDataSourceFromS3Request)` Creates a `DataSource` object.
`CreateEvaluationResult`	`createEvaluation(CreateEvaluationRequest createEvaluationRequest)` Creates a new `Evaluation` of an `MLModel`.
`CreateMLModelResult`	`createMLModel(CreateMLModelRequest createMLModelRequest)` Creates a new `MLModel` using the `DataSource` and the recipe as information sources.
`CreateRealtimeEndpointResult`	`createRealtimeEndpoint(CreateRealtimeEndpointRequest createRealtimeEndpointRequest)` Creates a real-time endpoint for the `MLModel`.
`DeleteBatchPredictionResult`	`deleteBatchPrediction(DeleteBatchPredictionRequest deleteBatchPredictionRequest)` Assigns the DELETED status to a `BatchPrediction`, rendering it unusable.
`DeleteDataSourceResult`	`deleteDataSource(DeleteDataSourceRequest deleteDataSourceRequest)` Assigns the DELETED status to a `DataSource`, rendering it unusable.
`DeleteEvaluationResult`	`deleteEvaluation(DeleteEvaluationRequest deleteEvaluationRequest)` Assigns the `DELETED` status to an `Evaluation`, rendering it unusable.
`DeleteMLModelResult`	`deleteMLModel(DeleteMLModelRequest deleteMLModelRequest)` Assigns the `DELETED` status to an `MLModel`, rendering it unusable.
`DeleteRealtimeEndpointResult`	`deleteRealtimeEndpoint(DeleteRealtimeEndpointRequest deleteRealtimeEndpointRequest)` Deletes a real time endpoint of an `MLModel`.
`DeleteTagsResult`	`deleteTags(DeleteTagsRequest deleteTagsRequest)` Deletes the specified tags associated with an ML object.
`DescribeBatchPredictionsResult`	`describeBatchPredictions()` Simplified method form for invoking the DescribeBatchPredictions operation.
`DescribeBatchPredictionsResult`	`describeBatchPredictions(DescribeBatchPredictionsRequest describeBatchPredictionsRequest)` Returns a list of `BatchPrediction` operations that match the search criteria in the request.
`DescribeDataSourcesResult`	`describeDataSources()` Simplified method form for invoking the DescribeDataSources operation.
`DescribeDataSourcesResult`	`describeDataSources(DescribeDataSourcesRequest describeDataSourcesRequest)` Returns a list of `DataSource` that match the search criteria in the request.
`DescribeEvaluationsResult`	`describeEvaluations()` Simplified method form for invoking the DescribeEvaluations operation.
`DescribeEvaluationsResult`	`describeEvaluations(DescribeEvaluationsRequest describeEvaluationsRequest)` Returns a list of `DescribeEvaluations` that match the search criteria in the request.
`DescribeMLModelsResult`	`describeMLModels()` Simplified method form for invoking the DescribeMLModels operation.
`DescribeMLModelsResult`	`describeMLModels(DescribeMLModelsRequest describeMLModelsRequest)` Returns a list of `MLModel` that match the search criteria in the request.
`DescribeTagsResult`	`describeTags(DescribeTagsRequest describeTagsRequest)` Describes one or more of the tags for your Amazon ML object.
`GetBatchPredictionResult`	`getBatchPrediction(GetBatchPredictionRequest getBatchPredictionRequest)` Returns a `BatchPrediction` that includes detailed metadata, status, and data file information for a `Batch Prediction` request.
`ResponseMetadata`	`getCachedResponseMetadata(AmazonWebServiceRequest request)` Returns additional metadata for a previously executed successful request, typically used for debugging issues where a service isn't acting as expected.
`GetDataSourceResult`	`getDataSource(GetDataSourceRequest getDataSourceRequest)` Returns a `DataSource` that includes metadata and data file information, as well as the current status of the `DataSource` .
`GetEvaluationResult`	`getEvaluation(GetEvaluationRequest getEvaluationRequest)` Returns an `Evaluation` that includes metadata as well as the current status of the `Evaluation`.
`GetMLModelResult`	`getMLModel(GetMLModelRequest getMLModelRequest)` Returns an `MLModel` that includes detailed metadata, data source information, and the current status of the `MLModel`.
`PredictResult`	`predict(PredictRequest predictRequest)` Generates a prediction for the observation using the specified `ML Model`.
`void`	`setEndpoint(String endpoint)` Overrides the default endpoint for this client ("https://machinelearning.us-east-1.amazonaws.com").
`void`	`setRegion(Region region)` An alternative to `setEndpoint(String)`, sets the regional endpoint for this client's service calls.
`void`	`shutdown()` Shuts down this client object, releasing any resources that might be held open.
`UpdateBatchPredictionResult`	`updateBatchPrediction(UpdateBatchPredictionRequest updateBatchPredictionRequest)` Updates the `BatchPredictionName` of a `BatchPrediction`.
`UpdateDataSourceResult`	`updateDataSource(UpdateDataSourceRequest updateDataSourceRequest)` Updates the `DataSourceName` of a `DataSource`.
`UpdateEvaluationResult`	`updateEvaluation(UpdateEvaluationRequest updateEvaluationRequest)` Updates the `EvaluationName` of an `Evaluation`.
`UpdateMLModelResult`	`updateMLModel(UpdateMLModelRequest updateMLModelRequest)` Updates the `MLModelName` and the `ScoreThreshold` of an `MLModel`.
`AmazonMachineLearningWaiters`	`waiters()`

- Field Detail
  - ENDPOINT_PREFIX
```
static final String ENDPOINT_PREFIX
```
    The region metadata service name for computing region endpoints. You can use this value to retrieve metadata (such as supported regions) of the service.
    
    See Also:
    
    RegionUtils.getRegionsForService(String), Constant Field Values
- Method Detail
  - setEndpoint
```
void setEndpoint(String endpoint)
```
    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 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.
    
    Parameters:
    
    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.
  - setRegion
```
void setRegion(Region region)
```
    An alternative to 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 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.
    
    Parameters:
    
    region - The region this client will communicate with. See 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 Also:
    
    Region.getRegion(com.amazonaws.regions.Regions), Region.createClient(Class, com.amazonaws.auth.AWSCredentialsProvider, ClientConfiguration), Region.isServiceSupported(String)
  - addTags
```
AddTagsResult addTags(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.
    
    Parameters:
    
    addTagsRequest -
    
    Returns:
    
    Result of the AddTags operation returned by the service.
    
    Throws:
    
    InvalidInputException - An error on the client occurred. Typically, the cause is an invalid input value.
    
    InvalidTagException
    
    TagLimitExceededException
    
    ResourceNotFoundException - A specified resource cannot be located.
    
    InternalServerException - An error on the server occurred when trying to process a request.
  - createBatchPrediction
```
CreateBatchPredictionResult createBatchPrediction(CreateBatchPredictionRequest createBatchPredictionRequest)
```
    Generates predictions for a group of observations. The observations to process exist in one or more data files referenced by a DataSource. This operation creates a new BatchPrediction, and uses an MLModel and the data files referenced by the DataSource as information sources.
    
    CreateBatchPrediction is an asynchronous operation. In response to CreateBatchPrediction, Amazon Machine Learning (Amazon ML) immediately returns and sets the BatchPrediction status to PENDING. After the BatchPrediction completes, Amazon ML sets the status to COMPLETED.
    
    You can poll for status updates by using the GetBatchPrediction operation and checking the Status parameter of the result. After the COMPLETED status appears, the results are available in the location specified by the OutputUri parameter.
    
    Parameters:
    
    createBatchPredictionRequest -
    
    Returns:
    
    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.
    
    InternalServerException - An error on the server occurred when trying to process a request.
    
    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.
  - createDataSourceFromRDS
```
CreateDataSourceFromRDSResult createDataSourceFromRDS(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.
    
    Parameters:
    
    createDataSourceFromRDSRequest -
    
    Returns:
    
    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.
    
    InternalServerException - An error on the server occurred when trying to process a request.
    
    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.
  - createDataSourceFromRedshift
```
CreateDataSourceFromRedshiftResult createDataSourceFromRedshift(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.
    
    Parameters:
    
    createDataSourceFromRedshiftRequest -
    
    Returns:
    
    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.
    
    InternalServerException - An error on the server occurred when trying to process a request.
    
    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.
  - createDataSourceFromS3
```
CreateDataSourceFromS3Result createDataSourceFromS3(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.
    
    Parameters:
    
    createDataSourceFromS3Request -
    
    Returns:
    
    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.
    
    InternalServerException - An error on the server occurred when trying to process a request.
    
    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.
  - createEvaluation
```
CreateEvaluationResult createEvaluation(CreateEvaluationRequest createEvaluationRequest)
```
    Creates a new Evaluation of an MLModel. An MLModel is evaluated on a set of observations associated to a DataSource. Like a DataSource for an MLModel, the DataSource for an Evaluation contains values for the Target Variable. The Evaluation compares the predicted result for each observation to the actual outcome and provides a summary so that you know how effective the MLModel functions on the test data. Evaluation generates a relevant performance metric, such as BinaryAUC, RegressionRMSE or MulticlassAvgFScore based on the corresponding MLModelType: BINARY, REGRESSION or MULTICLASS.
    
    CreateEvaluation is an asynchronous operation. In response to CreateEvaluation, Amazon Machine Learning (Amazon ML) immediately returns and sets the evaluation status to PENDING. After the Evaluation is created and ready for use, Amazon ML sets the status to COMPLETED.
    
    You can use the GetEvaluation operation to check progress of the evaluation during the creation operation.
    
    Parameters:
    
    createEvaluationRequest -
    
    Returns:
    
    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.
    
    InternalServerException - An error on the server occurred when trying to process a request.
    
    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.
  - createMLModel
```
CreateMLModelResult createMLModel(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.
    
    Parameters:
    
    createMLModelRequest -
    
    Returns:
    
    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.
    
    InternalServerException - An error on the server occurred when trying to process a request.
    
    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.
  - createRealtimeEndpoint
```
CreateRealtimeEndpointResult createRealtimeEndpoint(CreateRealtimeEndpointRequest createRealtimeEndpointRequest)
```
    Creates a real-time endpoint for the MLModel. The endpoint contains the URI of the MLModel; that is, the location to send real-time prediction requests for the specified MLModel .
    
    Parameters:
    
    createRealtimeEndpointRequest -
    
    Returns:
    
    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.
    
    ResourceNotFoundException - A specified resource cannot be located.
    
    InternalServerException - An error on the server occurred when trying to process a request.
  - deleteBatchPrediction
```
DeleteBatchPredictionResult deleteBatchPrediction(DeleteBatchPredictionRequest deleteBatchPredictionRequest)
```
    Assigns the DELETED status to a BatchPrediction, rendering it unusable.
    
    After using the DeleteBatchPrediction operation, you can use the GetBatchPrediction operation to verify that the status of the BatchPrediction changed to DELETED.
    
    Caution: The result of the DeleteBatchPrediction operation is irreversible.
    
    Parameters:
    
    deleteBatchPredictionRequest -
    
    Returns:
    
    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.
    
    ResourceNotFoundException - A specified resource cannot be located.
    
    InternalServerException - An error on the server occurred when trying to process a request.
  - deleteDataSource
```
DeleteDataSourceResult deleteDataSource(DeleteDataSourceRequest deleteDataSourceRequest)
```
    Assigns the DELETED status to a DataSource, rendering it unusable.
    
    After using the DeleteDataSource operation, you can use the GetDataSource operation to verify that the status of the DataSource changed to DELETED.
    
    Caution: The results of the DeleteDataSource operation are irreversible.
    
    Parameters:
    
    deleteDataSourceRequest -
    
    Returns:
    
    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.
    
    ResourceNotFoundException - A specified resource cannot be located.
    
    InternalServerException - An error on the server occurred when trying to process a request.
  - deleteEvaluation
```
DeleteEvaluationResult deleteEvaluation(DeleteEvaluationRequest deleteEvaluationRequest)
```
    Assigns the DELETED status to an Evaluation, rendering it unusable.
    
    After invoking the DeleteEvaluation operation, you can use the GetEvaluation operation to verify that the status of the Evaluation changed to DELETED.
    Caution
    The results of the DeleteEvaluation operation are irreversible.
    
    Parameters:
    
    deleteEvaluationRequest -
    
    Returns:
    
    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.
    
    ResourceNotFoundException - A specified resource cannot be located.
    
    InternalServerException - An error on the server occurred when trying to process a request.
  - deleteMLModel
```
DeleteMLModelResult deleteMLModel(DeleteMLModelRequest deleteMLModelRequest)
```
    Assigns the DELETED status to an MLModel, rendering it unusable.
    
    After using the DeleteMLModel operation, you can use the GetMLModel operation to verify that the status of the MLModel changed to DELETED.
    
    Caution: The result of the DeleteMLModel operation is irreversible.
    
    Parameters:
    
    deleteMLModelRequest -
    
    Returns:
    
    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.
    
    ResourceNotFoundException - A specified resource cannot be located.
    
    InternalServerException - An error on the server occurred when trying to process a request.
  - deleteRealtimeEndpoint
```
DeleteRealtimeEndpointResult deleteRealtimeEndpoint(DeleteRealtimeEndpointRequest deleteRealtimeEndpointRequest)
```
    Deletes a real time endpoint of an MLModel.
    
    Parameters:
    
    deleteRealtimeEndpointRequest -
    
    Returns:
    
    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.
    
    ResourceNotFoundException - A specified resource cannot be located.
    
    InternalServerException - An error on the server occurred when trying to process a request.
  - deleteTags
```
DeleteTagsResult deleteTags(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.
    
    Parameters:
    
    deleteTagsRequest -
    
    Returns:
    
    Result of the DeleteTags operation returned by the service.
    
    Throws:
    
    InvalidInputException - An error on the client occurred. Typically, the cause is an invalid input value.
    
    InvalidTagException
    
    ResourceNotFoundException - A specified resource cannot be located.
    
    InternalServerException - An error on the server occurred when trying to process a request.
  - describeBatchPredictions
```
DescribeBatchPredictionsResult describeBatchPredictions(DescribeBatchPredictionsRequest describeBatchPredictionsRequest)
```
    Returns a list of BatchPrediction operations that match the search criteria in the request.
    
    Parameters:
    
    describeBatchPredictionsRequest -
    
    Returns:
    
    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.
    
    InternalServerException - An error on the server occurred when trying to process a request.
  - describeBatchPredictions
```
DescribeBatchPredictionsResult describeBatchPredictions()
```
    Simplified method form for invoking the DescribeBatchPredictions operation.
    
    See Also:
    
    describeBatchPredictions(DescribeBatchPredictionsRequest)
  - describeDataSources
```
DescribeDataSourcesResult describeDataSources(DescribeDataSourcesRequest describeDataSourcesRequest)
```
    Returns a list of DataSource that match the search criteria in the request.
    
    Parameters:
    
    describeDataSourcesRequest -
    
    Returns:
    
    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.
    
    InternalServerException - An error on the server occurred when trying to process a request.
  - describeDataSources
```
DescribeDataSourcesResult describeDataSources()
```
    Simplified method form for invoking the DescribeDataSources operation.
    
    See Also:
    
    describeDataSources(DescribeDataSourcesRequest)
  - describeEvaluations
```
DescribeEvaluationsResult describeEvaluations(DescribeEvaluationsRequest describeEvaluationsRequest)
```
    Returns a list of DescribeEvaluations that match the search criteria in the request.
    
    Parameters:
    
    describeEvaluationsRequest -
    
    Returns:
    
    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.
    
    InternalServerException - An error on the server occurred when trying to process a request.
  - describeEvaluations
```
DescribeEvaluationsResult describeEvaluations()
```
    Simplified method form for invoking the DescribeEvaluations operation.
    
    See Also:
    
    describeEvaluations(DescribeEvaluationsRequest)
  - describeMLModels
```
DescribeMLModelsResult describeMLModels(DescribeMLModelsRequest describeMLModelsRequest)
```
    Returns a list of MLModel that match the search criteria in the request.
    
    Parameters:
    
    describeMLModelsRequest -
    
    Returns:
    
    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.
    
    InternalServerException - An error on the server occurred when trying to process a request.
  - describeMLModels
```
DescribeMLModelsResult describeMLModels()
```
    Simplified method form for invoking the DescribeMLModels operation.
    
    See Also:
    
    describeMLModels(DescribeMLModelsRequest)
  - describeTags
```
DescribeTagsResult describeTags(DescribeTagsRequest describeTagsRequest)
```
    Describes one or more of the tags for your Amazon ML object.
    
    Parameters:
    
    describeTagsRequest -
    
    Returns:
    
    Result of the DescribeTags operation returned by the service.
    
    Throws:
    
    InvalidInputException - An error on the client occurred. Typically, the cause is an invalid input value.
    
    ResourceNotFoundException - A specified resource cannot be located.
    
    InternalServerException - An error on the server occurred when trying to process a request.
  - getBatchPrediction
```
GetBatchPredictionResult getBatchPrediction(GetBatchPredictionRequest getBatchPredictionRequest)
```
    Returns a BatchPrediction that includes detailed metadata, status, and data file information for a Batch Prediction request.
    
    Parameters:
    
    getBatchPredictionRequest -
    
    Returns:
    
    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.
    
    ResourceNotFoundException - A specified resource cannot be located.
    
    InternalServerException - An error on the server occurred when trying to process a request.
  - getDataSource
```
GetDataSourceResult getDataSource(GetDataSourceRequest getDataSourceRequest)
```
    Returns a DataSource that includes metadata and data file information, as well as the current status of the DataSource .
    
    GetDataSource provides results in normal or verbose format. The verbose format adds the schema description and the list of files pointed to by the DataSource to the normal format.
    
    Parameters:
    
    getDataSourceRequest -
    
    Returns:
    
    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.
    
    ResourceNotFoundException - A specified resource cannot be located.
    
    InternalServerException - An error on the server occurred when trying to process a request.
  - getEvaluation
```
GetEvaluationResult getEvaluation(GetEvaluationRequest getEvaluationRequest)
```
    Returns an Evaluation that includes metadata as well as the current status of the Evaluation.
    
    Parameters:
    
    getEvaluationRequest -
    
    Returns:
    
    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.
    
    ResourceNotFoundException - A specified resource cannot be located.
    
    InternalServerException - An error on the server occurred when trying to process a request.
  - getMLModel
```
GetMLModelResult getMLModel(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.
    
    Parameters:
    
    getMLModelRequest -
    
    Returns:
    
    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.
    
    ResourceNotFoundException - A specified resource cannot be located.
    
    InternalServerException - An error on the server occurred when trying to process a request.
  - predict
```
PredictResult predict(PredictRequest predictRequest)
```
    Generates a prediction for the observation using the specified ML Model.
    Note
    Not all response parameters will be populated. Whether a response parameter is populated depends on the type of model requested.
    
    Parameters:
    
    predictRequest -
    
    Returns:
    
    Result of the Predict operation returned by the service.
    
    Throws:
    
    InvalidInputException - An error on the client occurred. Typically, the cause is an invalid input value.
    
    ResourceNotFoundException - A specified resource cannot be located.
    
    LimitExceededException - The subscriber exceeded the maximum number of operations. This exception can occur when listing objects such as DataSource.
    
    InternalServerException - An error on the server occurred when trying to process a request.
    
    PredictorNotMountedException - The exception is thrown when a predict request is made to an unmounted MLModel.
  - updateBatchPrediction
```
UpdateBatchPredictionResult updateBatchPrediction(UpdateBatchPredictionRequest updateBatchPredictionRequest)
```
    Updates the BatchPredictionName of a BatchPrediction.
    
    You can use the GetBatchPrediction operation to view the contents of the updated data element.
    
    Parameters:
    
    updateBatchPredictionRequest -
    
    Returns:
    
    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.
    
    ResourceNotFoundException - A specified resource cannot be located.
    
    InternalServerException - An error on the server occurred when trying to process a request.
  - updateDataSource
```
UpdateDataSourceResult updateDataSource(UpdateDataSourceRequest updateDataSourceRequest)
```
    Updates the DataSourceName of a DataSource.
    
    You can use the GetDataSource operation to view the contents of the updated data element.
    
    Parameters:
    
    updateDataSourceRequest -
    
    Returns:
    
    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.
    
    ResourceNotFoundException - A specified resource cannot be located.
    
    InternalServerException - An error on the server occurred when trying to process a request.
  - updateEvaluation
```
UpdateEvaluationResult updateEvaluation(UpdateEvaluationRequest updateEvaluationRequest)
```
    Updates the EvaluationName of an Evaluation.
    
    You can use the GetEvaluation operation to view the contents of the updated data element.
    
    Parameters:
    
    updateEvaluationRequest -
    
    Returns:
    
    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.
    
    ResourceNotFoundException - A specified resource cannot be located.
    
    InternalServerException - An error on the server occurred when trying to process a request.
  - updateMLModel
```
UpdateMLModelResult updateMLModel(UpdateMLModelRequest updateMLModelRequest)
```
    Updates the MLModelName and the ScoreThreshold of an MLModel.
    
    You can use the GetMLModel operation to view the contents of the updated data element.
    
    Parameters:
    
    updateMLModelRequest -
    
    Returns:
    
    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.
    
    ResourceNotFoundException - A specified resource cannot be located.
    
    InternalServerException - An error on the server occurred when trying to process a request.
  - shutdown
```
void shutdown()
```
    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.
  - getCachedResponseMetadata
```
ResponseMetadata getCachedResponseMetadata(AmazonWebServiceRequest request)
```
    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.
    
    Parameters:
    
    request - The originally executed request.
    
    Returns:
    
    The response metadata for the specified request, or null if none is available.
  - waiters
```
AmazonMachineLearningWaiters waiters()
```

Did this page help you?

Interface AmazonMachineLearning

Field Summary

Method Summary

Field Detail

ENDPOINT_PREFIX

Method Detail

setEndpoint

setRegion

addTags

createBatchPrediction

createDataSourceFromRDS

createDataSourceFromRedshift

createDataSourceFromS3

createEvaluation

createMLModel

createRealtimeEndpoint

deleteBatchPrediction

deleteDataSource

deleteEvaluation

deleteMLModel

deleteRealtimeEndpoint

deleteTags

describeBatchPredictions

describeBatchPredictions

describeDataSources

describeDataSources

describeEvaluations

describeEvaluations

describeMLModels

describeMLModels

describeTags

getBatchPrediction

getDataSource

getEvaluation

getMLModel

predict

updateBatchPrediction

updateDataSource

updateEvaluation

updateMLModel

shutdown

getCachedResponseMetadata

waiters