Monitoring Model Performance in Production
Data drift detection, model degradation signals, retraining triggers, evidently library.
Why Monitor in Production?
A model that performed well at launch can silently degrade as the world changes. Monitoring detects this drift early, before it quietly tanks business metrics, so you know when to retrain.
Data Drift vs Concept Drift
- Data drift: the input distribution changes (new customer mix).
- Concept drift: the relationship between inputs and target changes (what predicts churn shifts).
Both hurt accuracy; monitoring catches them in different ways.
All lessons in this course
- Experiment Tracking with MLflow
- Model Registry and Versioning
- Building Reproducible ML Pipelines
- Monitoring Model Performance in Production