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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.

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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