Your question is Post-Deployment Feedback Loop. 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 would you structure a feedback loop to continuously improve model performance post-deployment?
Explain how you would collect production signals and labels, detect data or feature drift, evaluate candidate model updates, and safely deploy improvements. Cover monitoring, retraining triggers, human or delayed feedback, rollback criteria, and controls for training-serving skew.