Model drift occurs when a machine‑learning model slowly loses accuracy because the data it sees in production changes over time. Causes include shifts in customer behaviour or new market conditions. Detecting drift early and retraining the model keeps performance high, reduces errors, and ensures fair and reliable results in ongoing operations.
