How do you handle concept drift in production machine learning systems?

Concept drift occurs when the statistical properties of target variables change over time, degrading model performance.\n\n• Detection: Statistical tests, performance monitoring, drift detection algorithms\n• Adaptation: Incremental learning, model retraining, ensemble updates\n• Monitoring: Track prediction accuracy, feature distributions, business metrics\n• Architecture: Online learning, sliding windows, A/B testing framework\n\nExample: E-commerce recommendation system monitors click-through rates, detects seasonal patterns, and retrains models weekly. Use drift detection algorithms like ADWIN, implement gradual model updates, and maintain fallback strategies.