计划数据质量监控作业

创建基准后，您可以调用 DefaultModelMonitor 类实例的 create_monitoring_schedule() 方法来计划每小时一次的数据质量监控。以下几节介绍如何为部署到实时端点的模型以及为批量转换作业创建数据质量监控。

对部署到实时端点的模型进行数据质量监控

要为实时端点计划数据质量监控，请将 EndpointInput 实例传递给 DefaultModelMonitor 实例的 endpoint_input 参数，如以下代码示例所示：

from sagemaker.model_monitor import CronExpressionGenerator
                
data_quality_model_monitor = DefaultModelMonitor(
   role=sagemaker.get_execution_role(),
   ...
)

schedule = data_quality_model_monitor.create_monitoring_schedule(
   monitor_schedule_name=schedule_name,
   post_analytics_processor_script=s3_code_postprocessor_uri,
   output_s3_uri=s3_report_path,
   schedule_cron_expression=CronExpressionGenerator.hourly(),
   statistics=data_quality_model_monitor.baseline_statistics(),
   constraints=data_quality_model_monitor.suggested_constraints(),
   schedule_cron_expression=CronExpressionGenerator.hourly(),
   enable_cloudwatch_metrics=True,
   endpoint_input=EndpointInput(
        endpoint_name=endpoint_name,
        destination="/opt/ml/processing/input/endpoint",
   )
)

对批量转换作业进行数据质量监控

要为批量转换作业计划数据质量监控，请将 BatchTransformInput 实例传递给 DefaultModelMonitor 实例的 batch_transform_input 参数，如以下代码示例所示：

from sagemaker.model_monitor import CronExpressionGenerator
                
data_quality_model_monitor = DefaultModelMonitor(
   role=sagemaker.get_execution_role(),
   ...
)

schedule = data_quality_model_monitor.create_monitoring_schedule(
    monitor_schedule_name=mon_schedule_name,
    batch_transform_input=BatchTransformInput(
        data_captured_destination_s3_uri=s3_capture_upload_path,
        destination="/opt/ml/processing/input",
        dataset_format=MonitoringDatasetFormat.csv(header=False),
    ),
    output_s3_uri=s3_report_path,
    statistics= statistics_path,
    constraints = constraints_path,
    schedule_cron_expression=CronExpressionGenerator.hourly(),
    enable_cloudwatch_metrics=True,
)