Drift Monitoring
The continuous monitoring of data and model behavior to detect changes that could reduce prediction accuracy or model performance over time.
What is Drift Monitoring?
Drift monitoring tracks whether production data and model outputs continue to behave as expected after deployment. It can identify changes such as data drift, where input distributions shift, or concept drift, where relationships between inputs and outcomes change. Teams typically monitor statistical patterns, model metrics, and prediction distributions to identify significant deviations.
Why is Drift Monitoring Important?
Machine learning models operate in environments that can change over time. Without monitoring, these changes may gradually reduce model accuracy and reliability without being immediately noticed. Drift monitoring provides early warning signals that help teams investigate issues and determine whether a model needs recalibration, retraining, or replacement.
Common use cases
Drift monitoring is commonly used in fraud detection, recommendation systems, credit scoring, forecasting, healthcare AI, and production machine learning systems.