Your question is Managing Model Drift and Retraining. 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 retraining in a high-volume production environment?
Explain how you would distinguish data drift, concept drift, and performance degradation; monitor them at scale; decide when retraining is justified; and deploy a replacement model safely. Address delayed labels, retraining frequency, validation, rollback, training-serving skew, and safeguards against feedback loops. Provide production-quality Python that demonstrates drift monitoring, time-aware retraining evaluation, and promotion logic without assuming a specific business domain.