Explain concept drift and how to handle it in production ML systems.

Concept drift occurs when statistical properties of target variables change over time, degrading model performance.\n\n• Detection: Statistical tests, performance monitoring, drift detection algorithms\n• Types: Sudden, gradual, recurring, incremental drift patterns\n• Adaptation: Model retraining, online learning, ensemble updates\n• Monitoring: Track prediction accuracy, feature distributions, business metrics\n\nExample: Credit scoring model detects drift during economic recession through declining precision. Implements automated retraining pipeline, uses ensemble of models from different time periods, and maintains champion-challenger framework for continuous improvement.