How do you monitor model drift and data drift in production?

Drift monitoring tracks changes in inputs and outputs over time. Monitor: - Input feature distributions - Prediction distribution - Performance proxy metrics - Ground-truth metrics when labels arrive Use alerts with thresholds and investigate root causes (product changes, seasonality, data bugs). Drift monitoring should trigger analysis, not automatic retraining by default.