The Misconception of Retraining: Why Model Refresh Isn’t Always the Fix

The article discusses the misconception that retraining a machine learning model is always the solution when performance drops. It argues that retraining can sometimes worsen the problem by injecting noise or hardcoding incorrect assumptions. The author suggests focusing on fixing feature logic, understanding user cohort evolution, and monitoring post-prediction KPIs instead of blindly retraining. The article emphasizes that a well-maintained system is one where you can identify and fix the root cause of issues, rather than simply replacing parts.