Model Drift

Model drift happens when a model's performance slides downhill because the real‑world data it encounters gradually differs from the data it was trained on. It's like a GPS that was calibrated for downtown streets but is now being used in a new suburb—the directions get less accurate over time. Detecting drift means regularly checking the model against fresh data and retraining when the error climbs beyond an acceptable threshold.