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ML Drift Detection Basics
Catch data and model drift
Detecting drift means monitoring feature distributions, target drift, and performance drops, setting thresholds and alerts, and responding with retraining or recalibration before quality degrades.
- drift detection
- mlops
- data drift
- concept drift
- retraining
Monitor Features
Track distribution shifts vs. training data; alert on significant change.
Watch Performance
Use labels/feedback when available; proxy metrics otherwise.
Respond
Retrain, recalibrate, or roll back; log drift events.
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