Explain the difference between data drift and concept drift, and how you would detect each.

What the interviewer wants to hear

• Data drift is a change in input distributions
• Concept drift is a change in the input to output relationship
• Data drift can be detected without labels using PSI or KS tests
• Concept drift usually needs labels or proxy outcomes

Model answer

Data drift means the inputs change: for example, a new marketing campaign brings in younger users, so the age distribution shifts. Concept drift means the relationship between inputs and the target changes: the same transaction pattern that used to be safe is now fraud because fraudsters adapted. Data drift can be detected without labels by comparing live feature and prediction distributions with the training data, using PSI, KS tests or a classifier that tries to tell the two apart. Concept drift is harder because inputs may look normal. It usually shows up as falling performance once labels arrive, so I track metrics over time on delayed labels and use proxy signals such as complaint rates in the meantime. In both cases I alert on meaningful changes in important features or model performance, not on every small statistical difference.

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