[ aws . machinelearning ]



Updates the MLModelName and the ScoreThreshold of an MLModel .

You can use the GetMLModel operation to view the contents of the updated data element.

See also: AWS API Documentation

See ‘aws help’ for descriptions of global parameters.


--ml-model-id <value>
[--ml-model-name <value>]
[--score-threshold <value>]
[--cli-input-json | --cli-input-yaml]
[--generate-cli-skeleton <value>]
[--cli-auto-prompt <value>]


--ml-model-id (string)

The ID assigned to the MLModel during creation.

--ml-model-name (string)

A user-supplied name or description of the MLModel .

--score-threshold (float)

The ScoreThreshold used in binary classification MLModel that marks the boundary between a positive prediction and a negative prediction.

Output values greater than or equal to the ScoreThreshold receive a positive result from the MLModel , such as true . Output values less than the ScoreThreshold receive a negative response from the MLModel , such as false .

--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.

--cli-auto-prompt (boolean) Automatically prompt for CLI input parameters.

See ‘aws help’ for descriptions of global parameters.


MLModelId -> (string)

The ID assigned to the MLModel during creation. This value should be identical to the value of the MLModelID in the request.