src/sagemaker/model_monitor/clarify_model_monitoring.py [506:555]:
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        analysis_config=None,
        output_s3_uri=None,
        constraints=None,
        monitor_schedule_name=None,
        schedule_cron_expression=None,
        enable_cloudwatch_metrics=True,
    ):
        """Creates a monitoring schedule.

        Args:
            endpoint_input (str or sagemaker.model_monitor.EndpointInput): The endpoint to monitor.
                This can either be the endpoint name or an EndpointInput.
            ground_truth_input (str): S3 URI to ground truth dataset.
            analysis_config (str or BiasAnalysisConfig): URI to analysis_config for the bias job.
                If it is None then configuration of the latest baselining job will be reused, but
                if no baselining job then fail the call.
            output_s3_uri (str): S3 destination of the constraint_violations and analysis result.
                Default: "s3://<default_session_bucket>/<job_name>/output"
            constraints (sagemaker.model_monitor.Constraints or str): If provided it will be used
                for monitoring the endpoint. It can be a Constraints object or an S3 uri pointing
                to a constraints JSON file.
            monitor_schedule_name (str): Schedule name. If not specified, the processor generates
                a default job name, based on the image name and current timestamp.
            schedule_cron_expression (str): The cron expression that dictates the frequency that
                this job run. See sagemaker.model_monitor.CronExpressionGenerator for valid
                expressions. Default: Daily.
            enable_cloudwatch_metrics (bool): Whether to publish cloudwatch metrics as part of
                the baselining or monitoring jobs.
        """
        if self.job_definition_name is not None or self.monitoring_schedule_name is not None:
            message = (
                "It seems that this object was already used to create an Amazon Model "
                "Monitoring Schedule. To create another, first delete the existing one "
                "using my_monitor.delete_monitoring_schedule()."
            )
            _LOGGER.error(message)
            raise ValueError(message)

        # create job definition
        monitor_schedule_name = self._generate_monitoring_schedule_name(
            schedule_name=monitor_schedule_name
        )
        new_job_definition_name = name_from_base(self.JOB_DEFINITION_BASE_NAME)
        request_dict = self._build_create_job_definition_request(
            monitoring_schedule_name=monitor_schedule_name,
            job_definition_name=new_job_definition_name,
            image_uri=self.image_uri,
            latest_baselining_job_name=self.latest_baselining_job_name,
            latest_baselining_job_config=self.latest_baselining_job_config,
            endpoint_input=endpoint_input,
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src/sagemaker/model_monitor/clarify_model_monitoring.py [900:948]:
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        analysis_config=None,
        output_s3_uri=None,
        constraints=None,
        monitor_schedule_name=None,
        schedule_cron_expression=None,
        enable_cloudwatch_metrics=True,
    ):
        """Creates a monitoring schedule.

        Args:
            endpoint_input (str or sagemaker.model_monitor.EndpointInput): The endpoint to monitor.
                This can either be the endpoint name or an EndpointInput.
            analysis_config (str or ExplainabilityAnalysisConfig): URI to the analysis_config for
                the explainability job. If it is None then configuration of the latest baselining
                job will be reused, but if no baselining job then fail the call.
            output_s3_uri (str): S3 destination of the constraint_violations and analysis result.
                Default: "s3://<default_session_bucket>/<job_name>/output"
            constraints (sagemaker.model_monitor.Constraints or str): If provided it will be used
                for monitoring the endpoint. It can be a Constraints object or an S3 uri pointing
                to a constraints JSON file.
            monitor_schedule_name (str): Schedule name. If not specified, the processor generates
                a default job name, based on the image name and current timestamp.
            schedule_cron_expression (str): The cron expression that dictates the frequency that
                this job run. See sagemaker.model_monitor.CronExpressionGenerator for valid
                expressions. Default: Daily.
            enable_cloudwatch_metrics (bool): Whether to publish cloudwatch metrics as part of
                the baselining or monitoring jobs.
        """
        if self.job_definition_name is not None or self.monitoring_schedule_name is not None:
            message = (
                "It seems that this object was already used to create an Amazon Model "
                "Monitoring Schedule. To create another, first delete the existing one "
                "using my_monitor.delete_monitoring_schedule()."
            )
            _LOGGER.error(message)
            raise ValueError(message)

        # create job definition
        monitor_schedule_name = self._generate_monitoring_schedule_name(
            schedule_name=monitor_schedule_name
        )
        new_job_definition_name = name_from_base(self.JOB_DEFINITION_BASE_NAME)
        request_dict = self._build_create_job_definition_request(
            monitoring_schedule_name=monitor_schedule_name,
            job_definition_name=new_job_definition_name,
            image_uri=self.image_uri,
            latest_baselining_job_name=self.latest_baselining_job_name,
            latest_baselining_job_config=self.latest_baselining_job_config,
            endpoint_input=endpoint_input,
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