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ML Feature Drift Mitigation

React when features shift

Handling feature drift means monitoring distributions, alerting on shifts, retraining or recalibrating models, using more stable features, and adding guards like fallbacks or human review for high-risk cases.

Detect and Alert

Track feature distributions vs training; alert on shifts.

Respond

Retrain, recalibrate thresholds, or freeze risky features.

Harden

Favor stable features; add fallbacks/human review where needed.

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