Detecting Input Drift with PSI Before Model Accuracy Degrades

Deployed machine learning models can lose accuracy even when their code and weights stay unchanged, because the data they receive shifts over time. Ground-truth labels often arrive late, so monitoring input and output distributions offers an earlier warning sign. The article recommends using the Population Stability Index (PSI) to compare live data against a fixed, deliberately chosen baseline snapshot. It explains the PSI formula, offers a NumPy implementation, and suggests common threshold conventions that should be calibrated to each system. The author also advises sanity-checking the metric and measuring its natural noise on small samples before setting alerts.