Instruction: Explain your approach to identifying concept drift in ML models and the strategies you employ to update models accordingly.
Context: This question is designed to understand how the candidate monitors and responds to concept drift in production models.
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The way I'd approach it in an interview is this: I monitor concept drift using delayed-label performance tracking, calibration checks, threshold behavior, segment-specific error rates, and business KPI changes where labels are slow. Unlike pure data drift, concept drift is...