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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.
This is an asynchronous operation using the standard naming convention for .NET 4.5 or higher. For .NET 3.5 the operation is implemented as a pair of methods using the standard naming convention of BeginCreateMLModel and EndCreateMLModel.
Namespace: Amazon.MachineLearning
Assembly: AWSSDK.MachineLearning.dll
Version: 3.x.y.z
public abstract Task<CreateMLModelResponse> CreateMLModelAsync( CreateMLModelRequest request, CancellationToken cancellationToken )
Container for the necessary parameters to execute the CreateMLModel service method.
A cancellation token that can be used by other objects or threads to receive notice of cancellation.
| Exception | Condition |
|---|---|
| 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. |
| InternalServerException | An error on the server occurred when trying to process a request. |
| InvalidInputException | An error on the client occurred. Typically, the cause is an invalid input value. |
.NET Core App:
Supported in: 3.1
.NET Standard:
Supported in: 2.0
.NET Framework:
Supported in: 4.5