update_ml_model `async` ¶

update_ml_model(
    ml_model_id: EntityId,
    *,
    config_overrides: Optional[AsyncMachineLearningClientConfig] = None,
    ml_model_name: Optional[EntityName] = None,
    score_threshold: Optional[ScoreThreshold] = None,
) -> UpdateMLModelOutput

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:

ml_model_id (EntityId) –

The ID assigned to the MLModel during creation.
ml_model_name (Optional[EntityName], default: None ) –

A user-supplied name or description of the MLModel.
score_threshold (Optional[ScoreThreshold], default: None ) –

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.

Raises:

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.
ResourceNotFoundException –

A specified resource cannot be located.
UnknownServiceError –

The service returned an error code this client does not model.

Source code in docs_src/capo_machine_learning/_services/async_machine_learning.py

async def update_ml_model(
    self,
    ml_model_id: "capo_machine_learning.types.entity_id.EntityId",
    *,
    config_overrides: Optional[AsyncMachineLearningClientConfig] = None,
    ml_model_name: Optional[
        "capo_machine_learning.types.entity_name.EntityName"
    ] = None,
    score_threshold: Optional[
        "capo_machine_learning.types.score_threshold.ScoreThreshold"
    ] = None,
) -> "capo_machine_learning.types.update_ml_model_output.UpdateMLModelOutput":
    """<p>Updates the <code>MLModelName</code> and the <code>ScoreThreshold</code> of an <code>MLModel</code>.</p> <p>You can use the <code>GetMLModel</code> operation to view the contents of the updated data element.</p>

    Args:
        ml_model_id: <p>The ID assigned to the <code>MLModel</code> during creation.</p>
        ml_model_name: <p>A user-supplied name or description of the <code>MLModel</code>.</p>
        score_threshold: <p>The <code>ScoreThreshold</code> used in binary classification <code>MLModel</code> that marks the boundary between a positive prediction and a negative prediction.</p> <p>Output values greater than or equal to the <code>ScoreThreshold</code> receive a positive result from the <code>MLModel</code>, such as <code>true</code>. Output values less than the <code>ScoreThreshold</code> receive a negative response from the <code>MLModel</code>, such as <code>false</code>.</p>

    Raises:
        capo_machine_learning.errors.internal_server_exception.InternalServerException: <p>An error on the server occurred when trying to process a request.</p>
        capo_machine_learning.errors.invalid_input_exception.InvalidInputException: <p>An error on the client occurred. Typically, the cause is an invalid input value.</p>
        capo_machine_learning.errors.resource_not_found_exception.ResourceNotFoundException: <p>A specified resource cannot be located.</p>
        capo_machine_learning.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    async def _handler(
        req: "AsyncOperationRequest[capo_machine_learning.types.update_ml_model_input.UpdateMLModelInput]",
    ) -> AsyncOperationResponse[
        "capo_machine_learning.types.update_ml_model_output.UpdateMLModelOutput"
    ]:
        import capo_machine_learning._operations.amazon_ml_20141212.update_ml_model

        (
            output,
            http_response,
        ) = await capo_machine_learning._operations.amazon_ml_20141212.update_ml_model.async_update_ml_model(
            req.options, req.input
        )
        return AsyncOperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("UpdateMLModel")
    )
    input_: capo_machine_learning.types.update_ml_model_input.UpdateMLModelInput = {
        "ml_model_id": ml_model_id
    }
    if ml_model_name is not None:
        input_["ml_model_name"] = ml_model_name
    if score_threshold is not None:
        input_["score_threshold"] = score_threshold

    response = await aexecute_pipeline(
        AsyncOperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    await response.response.aclose()
    return response.output

update_ml_model async ¶

update_ml_model `async` ¶