Model Monitoring & Drift
Learn model monitoring techniques, drift detection, and performance tracking for production ML systems. This is a foundational concept in artificial intelligence and machine learning that professional developers rely on daily. The explanations below are written to be beginner-friendly while covering the depth and nuance that comes from real-world AI/ML experience. Take your time with each section and practice the examples
45 min•By Priygop Team•Last updated: Feb 2026
Model Monitoring Types
- Performance Monitoring: Track model accuracy and metrics
- Data Drift Detection: Monitor input data distribution changes
- Concept Drift Detection: Monitor target variable changes
- Infrastructure Monitoring: Monitor system health and resources
Drift Detection Methods
- Statistical Methods: KS test, Chi-square test
- Distribution Comparison: KL divergence, Wasserstein distance
- Feature Drift: Monitor individual feature distributions
- Model Drift: Monitor model performance degradation
Monitoring Tools
- Evidently AI: Model monitoring and drift detection
- Weights & Biases: Experiment tracking and monitoring
- TensorBoard: TensorFlow model monitoring
- Grafana: Custom monitoring dashboards
Alerting & Response
- Threshold-based alerts: Performance degradation alerts
- Automated retraining: Trigger model retraining
- Fallback mechanisms: Switch to backup models
- Incident response: Automated incident handling