[ aws . machinelearning ]

create-ml-model

Description

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.

See also: AWS API Documentation

See ‘aws help’ for descriptions of global parameters.

Synopsis

  create-ml-model
--ml-model-id <value>
[--ml-model-name <value>]
--ml-model-type <value>
[--parameters <value>]
--training-data-source-id <value>
[--recipe <value>]
[--recipe-uri <value>]
[--cli-input-json | --cli-input-yaml]
[--generate-cli-skeleton <value>]

Options

--ml-model-id (string)

A user-supplied ID that uniquely identifies the MLModel .

--ml-model-name (string)

A user-supplied name or description of the MLModel .

--ml-model-type (string)

The category of supervised learning that this MLModel will address. Choose from the following types:

• Choose REGRESSION if the MLModel will be used to predict a numeric value.

• Choose BINARY if the MLModel result has two possible values.

• Choose MULTICLASS if the MLModel result has a limited number of values.

For more information, see the Amazon Machine Learning Developer Guide .

Possible values:

• REGRESSION

• BINARY

• MULTICLASS

--parameters (map)

A list of the training parameters in the MLModel . The list is implemented as a map of key-value pairs.

The following is the current set of training parameters:

• sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance. The value is an integer that ranges from 100000 to 2147483648 . The default value is 33554432 .

• sgd.maxPasses - The number of times that the training process traverses the observations to build the MLModel . The value is an integer that ranges from 1 to 10000 . The default value is 10 .

• sgd.shuffleType - Whether Amazon ML shuffles the training data. Shuffling the data improves a model’s ability to find the optimal solution for a variety of data types. The valid values are auto and none . The default value is none . We strongly recommend that you shuffle your data.

• sgd.l1RegularizationAmount - The coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, start by specifying a small value, such as 1.0E-08 . The value is a double that ranges from 0 to MAX_DOUBLE . The default is to not use L1 normalization. This parameter can’t be used when L2 is specified. Use this parameter sparingly.

• sgd.l2RegularizationAmount - The coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as 1.0E-08 . The value is a double that ranges from 0 to MAX_DOUBLE . The default is to not use L2 normalization. This parameter can’t be used when L1 is specified. Use this parameter sparingly.

key -> (string)

String type.

value -> (string)

String type.

Shorthand Syntax:

KeyName1=string,KeyName2=string

JSON Syntax:

{"string": "string"
  ...}

--training-data-source-id (string)

The DataSource that points to the training data.

--recipe (string)

The data recipe for creating the MLModel . You must specify either the recipe or its URI. If you don’t specify a recipe or its URI, Amazon ML creates a default.

--recipe-uri (string)

The Amazon Simple Storage Service (Amazon S3) location and file name that contains the MLModel recipe. You must specify either the recipe or its URI. If you don’t specify a recipe or its URI, Amazon ML creates a default.

--cli-input-json | --cli-input-yaml (string) Reads arguments from the JSON string provided. The JSON string follows the format provided by --generate-cli-skeleton. If other arguments are provided on the command line, those values will override the JSON-provided values. It is not possible to pass arbitrary binary values using a JSON-provided value as the string will be taken literally. This may not be specified along with --cli-input-yaml.

--generate-cli-skeleton (string) Prints a JSON skeleton to standard output without sending an API request. If provided with no value or the value input, prints a sample input JSON that can be used as an argument for --cli-input-json. Similarly, if provided yaml-input it will print a sample input YAML that can be used with --cli-input-yaml. If provided with the value output, it validates the command inputs and returns a sample output JSON for that command.

See ‘aws help’ for descriptions of global parameters.

Output

MLModelId -> (string)

A user-supplied ID that uniquely identifies the MLModel . This value should be identical to the value of the MLModelId in the request.