Concept drift occurs when the relationship between inputs and outputs changes over time, degrading model performance. Definition: P(Y|X) changes - same inputs now map to different outputs. The underlying patterns the model learned are no longer valid. Example: Customer buying behavior shifts due to economic changes, pandemic alters health data patterns, user preferences evolve. Concept drift vs data drift: Data drift is P(X) changing (input distribution). Concept drift is P(Y|X) changing (actual relationship). Both problematic. Detection methods: Monitor prediction accuracy with ground truth, statistical tests on residuals, track performance on labeled windows. Types: Sudden: Abrupt change (policy change, event). Gradual: Slow evolution over time. Recurring: Seasonal patterns. Incremental: Small continuous changes. Response: Retrain on recent data, use online learning, adaptive models, sliding window training. Prevention: Regular retraining schedules, continuous monitoring, domain expert alerts for known changes. Challenges: Ground truth delay makes detection slow, distinguishing drift from noise.