Schedule Bias Drift Monitoring Jobs

After you create your baseline, you can call the create_monitoring_schedule() method of your ModelBiasModelMonitor class instance to schedule an hourly bias drift monitor. The following sections show you how to create bias drift monitor for a model deployed to a real-time endpoint as well as for a batch transform job.

Important

You can specify either a batch transform input or an endpoint input, but not both, when you create your monitoring schedule.

Unlike data quality monitoring, you need to supply Ground Truth labels if you want to monitor model quality. However, Ground Truth labels could be delayed. To address this, specify offsets when you create your monitoring schedule. For details about how to create time offsets, see Model monitor offsets.

If you have submitted a baselining job, the monitor automatically picks up analysis configuration from the baselining job. If you skip the baselining step or the capture dataset has a different nature from the training dataset, you must provide the analysis configuration.

Bias drift monitoring for models deployed to real-time endpoint

To schedule a bias drift monitor for a real-time endpoint, pass your EndpointInput instance to the endpoint_input argument of your ModelBiasModelMonitor instance, as shown in the following code sample:

from sagemaker.model_monitor import CronExpressionGenerator
            
model_bias_monitor = ModelBiasModelMonitor(
    role=sagemaker.get_execution_role(),
    ...
)

model_bias_analysis_config = None
if not model_bias_monitor.latest_baselining_job:
    model_bias_analysis_config = BiasAnalysisConfig(
        model_bias_config,
        headers=all_headers,
        label=label_header,
    )

model_bias_monitor.create_monitoring_schedule(
    monitor_schedule_name=schedule_name,
    post_analytics_processor_script=s3_code_postprocessor_uri,
    output_s3_uri=s3_report_path,
    statistics=model_bias_monitor.baseline_statistics(),
    constraints=model_bias_monitor.suggested_constraints(),
    schedule_cron_expression=CronExpressionGenerator.hourly(),
    enable_cloudwatch_metrics=True,
    analysis_config=model_bias_analysis_config,
    endpoint_input=EndpointInput(
        endpoint_name=endpoint_name,
        destination="/opt/ml/processing/input/endpoint",
        start_time_offset="-PT1H",
        end_time_offset="-PT0H",
        probability_threshold_attribute=0.8,
    ),
)

Bias drift monitoring for batch transform jobs

To schedule a bias drift monitor for a batch transform job, pass your BatchTransformInput instance to the batch_transform_input argument of your ModelBiasModelMonitor instance, as shown in the following code sample:

from sagemaker.model_monitor import CronExpressionGenerator
                
model_bias_monitor = ModelBiasModelMonitor(
    role=sagemaker.get_execution_role(),
    ...
)

model_bias_analysis_config = None
if not model_bias_monitor.latest_baselining_job:
    model_bias_analysis_config = BiasAnalysisConfig(
        model_bias_config,
        headers=all_headers,
        label=label_header,
    )
    
schedule = model_bias_monitor.create_monitoring_schedule(
   monitor_schedule_name=schedule_name,
   post_analytics_processor_script=s3_code_postprocessor_uri,
   output_s3_uri=s3_report_path,
   statistics=model_bias_monitor.baseline_statistics(),
   constraints=model_bias_monitor.suggested_constraints(),
   schedule_cron_expression=CronExpressionGenerator.hourly(),
   enable_cloudwatch_metrics=True,
   analysis_config=model_bias_analysis_config,
   batch_transform_input=BatchTransformInput(
        destination="opt/ml/processing/input",
        data_captured_destination_s3_uri=s3_capture_path,
        start_time_offset="-PT1H",
        end_time_offset="-PT0H",
        probability_threshold_attribute=0.8
   ),
)