Stop Retraining Blindly: Use PSI to Build a Smarter Monitoring Pipeline

Deployed machine learning models can degrade silently as the distribution of real-world data changes, a phenomenon known as data drift. The Population Stability Index (PSI) is a statistical tool used to monitor and quantify this drift by comparing the distribution of new, incoming data against the original training data. PSI values are interpreted using common thresholds, where a score below 0.10 indicates stability while a score above 0.25 signals a major shift requiring investigation. The process involves bucketing data, calculating percentages in each bucket for both datasets, and using a formula to derive the final score, which is demonstrated with a Python function. Using PSI allows data science teams to detect data drift early without waiting for performance metrics to decline, enabling more proactive and intelligent model maintenance.