You've shipped a model that was performing acceptably at launch, but over time its production behavior starts to change. The team wants a clear plan for detecting drift, understanding whether it is feature shift, label shift, or score drift, and deciding when to recalibrate, retune thresholds, retrain, or roll back.
How would you manage model drift in a production AI system?