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delete_ml_model 

delete_ml_model(
    ml_model_id: EntityId,
    *,
    config_overrides: Optional[MachineLearningClientConfig] = None,
) -> DeleteMLModelOutput

Assigns the DELETED status to an MLModel, rendering it unusable.

After using the DeleteMLModel operation, you can use the GetMLModel operation to verify that the status of the MLModel changed to DELETED.

Caution: The result of the DeleteMLModel operation is irreversible.

Parameters:

  • ml_model_id (EntityId) –

    A user-supplied ID that uniquely identifies the MLModel.

Raises:

Source code in docs_src/capo_machine_learning/_services/machine_learning.py 
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def delete_ml_model(
    self,
    ml_model_id: "capo_machine_learning.types.entity_id.EntityId",
    *,
    config_overrides: Optional[MachineLearningClientConfig] = None,
) -> "capo_machine_learning.types.delete_ml_model_output.DeleteMLModelOutput":
    """<p>Assigns the <code>DELETED</code> status to an <code>MLModel</code>, rendering it unusable.</p> <p>After using the <code>DeleteMLModel</code> operation, you can use the <code>GetMLModel</code> operation to verify that the status of the <code>MLModel</code> changed to DELETED.</p> <p> <b>Caution:</b> The result of the <code>DeleteMLModel</code> operation is irreversible.</p>

    Args:
        ml_model_id: <p>A user-supplied ID that uniquely identifies the <code>MLModel</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.
    """

    def _handler(
        req: "OperationRequest[capo_machine_learning.types.delete_ml_model_input.DeleteMLModelInput]",
    ) -> OperationResponse[
        "capo_machine_learning.types.delete_ml_model_output.DeleteMLModelOutput"
    ]:
        import capo_machine_learning._operations.amazon_ml_20141212.delete_ml_model

        output, http_response = (
            capo_machine_learning._operations.amazon_ml_20141212.delete_ml_model.delete_ml_model(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("DeleteMLModel")
    )
    input_: capo_machine_learning.types.delete_ml_model_input.DeleteMLModelInput = {
        "ml_model_id": ml_model_id
    }

    response = execute_pipeline(
        OperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    response.response.close()
    return response.output