What is the best strategy for monitoring model drift once a model is deployed in production?

All answers to this question.

Monitoring drift is the "Ops" part of MLOps that people often forget. You should implement the Population Stability Index (PSI) to compare the distribution of your features in the training set versus the live data. If the PSI exceeds 0.2, it’s a clear sign of significant drift. For the output side, monitor the "Concept Drift" by tracking the error rate over time. Tools like Evidently AI or Amazon SageMaker Model Monitor can automate this. Don't just retrain on a schedule; retrain when the statistical tests tell you the underlying data distribution has changed.

   Answered 2025-11-07 by Diana Bishop

Are you currently logging all your inference data to a centralized warehouse so you can run these statistical comparisons retrospectively?

   Answered 2025-11-10 by Timothy Lane

• Timothy, we are logging everything to S3, but we haven't built a pipeline to analyze it yet. Is it better to run these checks in real-time as the API receives requests, or is a batch job at the end of every day sufficient for most e-commerce use cases involving user recommendations?

     Commented 2025-11-12 by Justin Perry

I highly recommend the Kolmogorov-Smirnov (KS) test for individual feature drift. It’s very sensitive and gives you a clear p-value to trigger alerts.

   Answered 2025-11-14 by Angela Meyer

• The KS test is excellent. Angela's suggestion combined with an automated dashboard makes it much easier for stakeholders to see why a model might be failing.

     Commented 2025-11-15 by Ryan Coleman