What is Model Decay?
Model Decay — The degradation of an AI model’s predictive performance over time due to changes in real-world data.
Model decay happens when the real world changes but the model stays static. A fraud detection model trained on 2022 data may miss new fraud patterns in 2024. Monitoring model performance over time and scheduling regular retraining cycles are essential for production AI.
Frequently Asked Questions
How do I know if my model is decaying?
Monitor key performance metrics (accuracy, precision, recall) over time. A consistent downward trend indicates decay. Set up automated alerts when metrics drop below acceptable thresholds.
How often should models be retrained?
It depends on how fast your domain changes. Financial fraud models may need monthly updates. Product recommendation models might need weekly retraining. Some stable domains can go quarterly.
Can I prevent model decay?
You cannot prevent it, but you can manage it. Implement continuous monitoring, automated retraining pipelines, and A/B testing frameworks to catch and correct decay early.