An ML model’s performance often unexpectedly degrades when they are deployed in real-world domains. It is very important to track the true model performance metrics from real-world data and react in time, to avoid the consequences of poor model performance.
Causes of model performance degradation include:
- Input data changes (various reasons)
- Concept drift
Learn more about how this can affect your model in production.Â