Utilizzare con un CreateModelAWS SDK

class Models:

    @staticmethod
    def create_model(
        lookoutvision_client,
        project_name,
        training_results,
        tag_key=None,
        tag_key_value=None,
    ):
        """
        Creates a version of a Lookout for Vision model.

        :param lookoutvision_client: A Boto3 Lookout for Vision client.
        :param project_name: The name of the project in which you want to create a
                             model.
        :param training_results: The Amazon S3 location where training results are stored.
        :param tag_key: The key for a tag to add to the model.
        :param tag_key_value - A value associated with the tag_key.
        return: The model status and version.
        """
        try:
            logger.info("Training model...")
            output_bucket, output_folder = training_results.replace("s3://", "").split(
                "/", 1
            )
            output_config = {
                "S3Location": {"Bucket": output_bucket, "Prefix": output_folder}
            }
            tags = []
            if tag_key is not None:
                tags = [{"Key": tag_key, "Value": tag_key_value}]

            response = lookoutvision_client.create_model(
                ProjectName=project_name, OutputConfig=output_config, Tags=tags
            )

            logger.info("ARN: %s", response["ModelMetadata"]["ModelArn"])
            logger.info("Version: %s", response["ModelMetadata"]["ModelVersion"])
            logger.info("Started training...")

            print("Training started. Training might take several hours to complete.")

            # Wait until training completes.
            finished = False
            status = "UNKNOWN"
            while finished is False:
                model_description = lookoutvision_client.describe_model(
                    ProjectName=project_name,
                    ModelVersion=response["ModelMetadata"]["ModelVersion"],
                )
                status = model_description["ModelDescription"]["Status"]

                if status == "TRAINING":
                    logger.info("Model training in progress...")
                    time.sleep(600)
                    continue

                if status == "TRAINED":
                    logger.info("Model was successfully trained.")
                else:
                    logger.info(
                        "Model training failed: %s ",
                        model_description["ModelDescription"]["StatusMessage"],
                    )
                finished = True
        except ClientError:
            logger.exception("Couldn't train model.")
            raise
        else:
            return status, response["ModelMetadata"]["ModelVersion"]