Schedule Feature Attribute Drift Monitoring Jobs - Amazon SageMaker AI
Feature attribution drift monitoring for models deployed to real-time endpointFeature attribution drift monitoring for batch transform jobs
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Schedule Feature Attribute Drift Monitoring Jobs

Note

After careful consideration, we have made the decision to close new customer access to Amazon Sagemaker Model Monitor, effective 7/30/26. Existing customers can continue to use the service as normal. AWS continues to invest in security and availability improvements for Model Monitor, but we do not plan to introduce new features. For more information, see Amazon SageMaker Model Monitor availability change.

After you create your SHAP baseline, you can call the create_monitoring_schedule() method of your ModelExplainabilityMonitor class instance to schedule an hourly model explainability monitor. The following sections show you how to create a model explainability 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.

If a baselining job has been submitted, the monitor automatically picks up analysis configuration from the baselining job. However, if you skip the baselining step or the capture dataset has a different nature from the training dataset, you have to provide the analysis configuration. ModelConfig is required by ExplainabilityAnalysisConfig for the same reason that it's required for the baselining job. Note that only features are required for computing feature attribution, so you should exclude Ground Truth labeling.

Feature attribution drift monitoring for models deployed to real-time endpoint

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

from sagemaker.model_monitor import CronExpressionGenerator
from sagemaker.core.helper.session_helper import get_execution_role

model_exp_model_monitor = ModelExplainabilityMonitor(
   role=get_execution_role(),
   ... 
)

schedule = model_exp_model_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_exp_model_monitor.baseline_statistics(),
   constraints=model_exp_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",
    )
)

Feature attribution drift monitoring for batch transform jobs

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

from sagemaker.model_monitor import CronExpressionGenerator
from sagemaker.core.helper.session_helper import get_execution_role

model_exp_model_monitor = ModelExplainabilityMonitor(
   role=get_execution_role(),
   ... 
)

schedule = model_exp_model_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_exp_model_monitor.baseline_statistics(),
   constraints=model_exp_model_monitor.suggested_constraints(),
   schedule_cron_expression=CronExpressionGenerator.hourly(),
   enable_cloudwatch_metrics=True,
   batch_transform_input=BatchTransformInput(
        destination="opt/ml/processing/data",
        model_name="batch-fraud-detection-model",
        input_manifests_s3_uri="s3://amzn-s3-demo-bucket/batch-fraud-detection/on-schedule-monitoring/in/",
        excludeFeatures="0",
   )
)