Instruction: Describe the methods to detect model drift and strategies to update models in response to it.
Context: This question tests the candidate's ability to ensure the long-term reliability and accuracy of machine learning models in dynamic environments.
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The way I'd approach it in an interview is this: I evaluate drift with time-aware monitoring rather than one static validation score. That means watching feature distributions, calibration, segment-level performance, business KPIs, and delayed-label quality over time. The goal is to see whether the model is aging in a way...