Your question is Model Drift Reliability. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
How do you handle model drift and ensure long-term reliability in production AI systems?
Explain a practical end-to-end approach covering data and concept drift detection, delayed-label performance monitoring, alert thresholds, retraining decisions, rollback, and governance. Include how you would distinguish genuine distribution change from data-quality or pipeline failures, prevent training-serving skew, and validate a replacement model before deployment. Assume the system uses a supervised classification model with scheduled batch predictions, but no specific dataset or business domain is provided.