Model Monitoring & Maintenance
Learn how to monitor ML models in production, detect data drift and model degradation, and build automated retraining systems.
50 min•By Priygop Team•Last updated: Feb 2026
Why Models Degrade in Production
- Data Drift: The statistical properties of input data change over time — customer behavior evolves, seasonal patterns shift, new product categories emerge
- Concept Drift: The relationship between features and target changes — what predicted churn last year might not predict churn this year
- Feature Drift: Upstream data sources change format, go stale, or introduce bugs — a feature that was numeric becomes categorical after a system update
- Training-Serving Skew: The preprocessing in training differs from serving — the #1 cause of silent model failures. Feature stores help prevent this
- Feedback Loops: Model predictions influence future data — a recommendation system that only shows popular items makes them more popular, reducing diversity
Monitoring Best Practices
- Input Monitoring: Track feature distributions, missing values, schema violations — catch data issues before they affect predictions
- Output Monitoring: Track prediction distribution, confidence scores, and output drift — sudden shifts signal model problems
- Performance Monitoring: Track accuracy/F1/AUC against ground truth when available — lag time varies (fraud: days, churn: months)
- Latency & Throughput: Monitor inference latency (P50, P95, P99) and throughput — performance degradation affects user experience
- Statistical Tests: Use KS test, PSI (Population Stability Index), or Jensen-Shannon divergence to quantify drift mathematically
- Alerting: Set thresholds for automatic alerts — 'If PSI > 0.2 for any feature, alert the ML team'. Page on-call for critical models