monitoring-data-drift

Detect feature distribution drift by comparing a stable reference window against current live inference traffic. This process applies per-feature metric selection, baseline-noise calibration, and cohort breakdowns to rank shifting features before any retraining.

Reduce false alarms on seasonal data by calibrating alert thresholds against baseline noise. This cooldowned alerting approach prevents triggering false drift warnings on high-variance or cohort-specific features during post-deployment monitoring.

Use PSI for continuous and ordinal features, Jensen-Shannon divergence for categoricals, and proportion checks for booleans. This per-feature metric selection ensures accurate distribution drift detection across mixed data types.

Find the root cause of model performance degradation by generating root-cause hypotheses from per-feature drift tables and attribution categories. Investigating cohort breakdowns identifies whether seasonal shifts or live inference traffic changes caused the drop.

Yes, cohort analysis supports seasonal and cohort-shift investigations by producing cohort breakdowns alongside per-feature drift tables. This isolates specific population segments experiencing distribution drift in deployed models.

Avoid retraining when data drift stems from seasonal baseline noise rather than genuine distribution shifts. Calibrated thresholds and cooldowned alerting ensure you only recommend retraining after validating root-cause hypotheses for the feature shift.