Package | Description |
---|---|
com.amazonaws.services.machinelearning |
Synchronous and asynchronous client classes for accessing AmazonMachineLearning.
|
com.amazonaws.services.machinelearning.model |
Classes modeling the various types represented by AmazonMachineLearning.
|
Modifier and Type | Method and Description |
---|---|
CreateMLModelResult |
AmazonMachineLearningClient.createMLModel(CreateMLModelRequest createMLModelRequest)
Creates a new
MLModel using the data files and the
recipe as information sources. |
CreateMLModelResult |
AmazonMachineLearning.createMLModel(CreateMLModelRequest createMLModelRequest)
Creates a new
MLModel using the data files and the
recipe as information sources. |
Future<CreateMLModelResult> |
AmazonMachineLearningAsyncClient.createMLModelAsync(CreateMLModelRequest createMLModelRequest)
Creates a new
MLModel using the data files and the
recipe as information sources. |
Future<CreateMLModelResult> |
AmazonMachineLearningAsync.createMLModelAsync(CreateMLModelRequest createMLModelRequest)
Creates a new
MLModel using the data files and the
recipe as information sources. |
Future<CreateMLModelResult> |
AmazonMachineLearningAsyncClient.createMLModelAsync(CreateMLModelRequest createMLModelRequest,
AsyncHandler<CreateMLModelRequest,CreateMLModelResult> asyncHandler)
Creates a new
MLModel using the data files and the
recipe as information sources. |
Future<CreateMLModelResult> |
AmazonMachineLearningAsync.createMLModelAsync(CreateMLModelRequest createMLModelRequest,
AsyncHandler<CreateMLModelRequest,CreateMLModelResult> asyncHandler)
Creates a new
MLModel using the data files and the
recipe as information sources. |
Modifier and Type | Method and Description |
---|---|
Future<CreateMLModelResult> |
AmazonMachineLearningAsyncClient.createMLModelAsync(CreateMLModelRequest createMLModelRequest,
AsyncHandler<CreateMLModelRequest,CreateMLModelResult> asyncHandler)
Creates a new
MLModel using the data files and the
recipe as information sources. |
Future<CreateMLModelResult> |
AmazonMachineLearningAsync.createMLModelAsync(CreateMLModelRequest createMLModelRequest,
AsyncHandler<CreateMLModelRequest,CreateMLModelResult> asyncHandler)
Creates a new
MLModel using the data files and the
recipe as information sources. |
Modifier and Type | Method and Description |
---|---|
CreateMLModelRequest |
CreateMLModelRequest.addParametersEntry(String key,
String value)
A list of the training parameters in the
MLModel . |
CreateMLModelRequest |
CreateMLModelRequest.clearParametersEntries()
Removes all the entries added into Parameters.
|
CreateMLModelRequest |
CreateMLModelRequest.clone() |
CreateMLModelRequest |
CreateMLModelRequest.withMLModelId(String mLModelId)
A user-supplied ID that uniquely identifies the
MLModel . |
CreateMLModelRequest |
CreateMLModelRequest.withMLModelName(String mLModelName)
A user-supplied name or description of the
MLModel . |
CreateMLModelRequest |
CreateMLModelRequest.withMLModelType(MLModelType mLModelType)
The category of supervised learning that this
MLModel
will address. |
CreateMLModelRequest |
CreateMLModelRequest.withMLModelType(String mLModelType)
The category of supervised learning that this
MLModel
will address. |
CreateMLModelRequest |
CreateMLModelRequest.withParameters(Map<String,String> parameters)
A list of the training parameters in the
MLModel . |
CreateMLModelRequest |
CreateMLModelRequest.withRecipe(String recipe)
The data recipe for creating
MLModel . |
CreateMLModelRequest |
CreateMLModelRequest.withRecipeUri(String recipeUri)
The Amazon Simple Storage Service (Amazon S3) location and file name
that contains the
MLModel recipe. |
CreateMLModelRequest |
CreateMLModelRequest.withTrainingDataSourceId(String trainingDataSourceId)
The
DataSource that points to the training data. |
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