CloudWatch Metrics for Feature Drift Analysis
This guide shows CloudWatch metrics and their properties that you can use for feature attribute drift analysis in SageMaker Clarify. Feature attribute drift monitoring jobs compute and publish two types of metrics:
-
The global SHAP value of each feature.
Note
The name of this metric appends the feature name provided by the job analysis configuration to
feature_
. For example,feature_X
is the global SHAP value for featureX
. -
The
ExpectedValue
of the metric.
These metrics are published to the following CloudWatch namespace:
For real-time endpoints:
aws/sagemaker/Endpoints/explainability-metrics
For batch transform jobs:
aws/sagemaker/ModelMonitoring/explainability-metrics
Each metric has the following properties:
-
Endpoint
: The name of the monitored endpoint, if applicable. -
MonitoringSchedule
: The name of the schedule for the monitoring job. -
ExplainabilityMethod
: The method used to compute Shapley values. ChooseKernelShap
. -
Label
: The name provided by job analysis configurationlabel_headers
, or a placeholder likelabel0
. -
ValueType
: The type of the value returned by the metric. Choose eitherGlobalShapValues
orExpectedValue
.
To stop the monitoring jobs from publishing metrics, set
publish_cloudwatch_metrics
to Disabled
in the
Environment
map of model explainability
job definition.