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Core
Drift Detection
Models decay as the world shifts. Data drift is a move in the input distribution (catchable without labels by comparing live features to a training reference with PSI or KS tests); concept drift is a change in the input-to-output relationship (usually needs labels, which often lag). The discipline is watching inputs and predictions as leading indicators, alerting on sustained shifts, and triggering retraining. AI, ML, and GenAI engineer interviews probe it because 'the model was great at launch and quietly got worse' is a top production failure.
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MLOps & ML EngineeringWalk me through concept drift, data drift, and label drift. Which one actually forces a retrain?→MLOps & ML EngineeringHow do you decide when to retrain a production model: on a schedule, or triggered by drift?→MLOps & ML EngineeringWhat should you monitor for an ML model in production (beyond uptime)?→MLOps & ML EngineeringYour ground-truth labels arrive weeks late. How do you monitor the model in the meantime?→MLOps & ML EngineeringHow do you design the triggers and cadence for retraining a fleet of production models?→System Design for AI in ProductionDesign a monitoring system for a fleet of 100+ production ML models.→
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