How would you investigate a deployed model whose performance has deteriorated?
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Model Answer & Deep-Dive Explanation
Verify the monitoring and label pipeline before assuming the model changed. Compare current and training feature distributions, missing values, schema, segment mix, and prediction scores. Distinguish data drift from a change in the relationship between inputs and outcomes, which requires suitable outcome evidence. Check training-serving skew and upstream releases. Use recent labeled data and time-aware evaluation to test remediation. Consider rollback, recalibration, threshold changes, or retraining according to the cause, and monitor delayed labels so short-term proxy metrics do not hide real harm.
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