Model Drift
Model drift is the gradual decline in a deployed machine-learning model's accuracy as the real-world data it sees diverges from the data it was trained on. Equipment ageing, process changes and new conditions all cause it.
An industrial model trained on past behaviour assumes the future resembles the past, but plants change: assets degrade, raw materials vary, setpoints shift and seasons turn. As the live data distribution moves away from the training distribution, predictions become less reliable — a phenomenon known as drift. Detecting it requires monitoring model performance and input statistics over time, and remedies range from periodic retraining to fully automated retraining pipelines within MLOps.