DriftGuard: Catching Data Drift Early with PSI and Amazon SageMaker
A hands-on MLOps mini-project using Python, scikit-learn and the Population Stability Index A model that performs well on launch day can quietly get worse as the real world changes. The inputs it sees in production slowly stop looking like the data it was trained on. This is called data drift , and it is one of the most common reasons deployed models fail. In this post I walk through DriftGuard ,…
This hands-on project demonstrates how to detect data drift early using the Population Stability Index (PSI) and Amazon SageMaker. After training a model, monitoring its performance in production becomes crucial as real-world data shifts. The PSI quantifies the degree of change in data distribution by comparing it to a baseline.
The code provided on GitHub simulates shifted production data and calculates PSI for each feature. Thresholds guide interpretation, with scores below 0.10 indicating no significant change, between 0.10 and 0.20 suggesting moderate shifts, and 0.20 or higher signaling significant drift. The project highlights that a single threshold may not be sufficient and that sample size impacts PSI reliability.
In practice, larger data windows and combining PSI with other tests would be prudent for production monitoring. The project underscores the importance of continuous model evaluation after deployment.
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