How do you detect data drift and concept drift in production, concretely?
Models decay silently as the world shifts, and 'monitor for drift' is too vague. What counts is the actual statistical methods and separating data drift you can detect without labels from concept drift you often cannot.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Models decay silently as the world shifts, and 'monitor for drift' is too vague. What counts is the actual statistical methods and separating data drift you can detect without labels from concept drift you often cannot.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.