The point about extending CI/CD to data and models is spot on. Most teams nail the code testing part but completely underestimate how often input data drifts or model behavior shifts in production. What helped me was treating data validation as a first-class citizen in the pipeline, not just an afterthought, because catching bad data early saves you from debugging model failures downstream.
The trickiest part of Model CI that nobody talks about is the brittle middle ground between automated tests passing and the model actually being production-ready. You can validate data schemas and code quality all day, but if your retrained model has a 2% drop in precision on edge cases that your test set doesn't capture, automated CI just waves it through. Most teams I've worked with end up building a hybrid approval gate after the CI pipeline, which sort of defeats the automation promise.
The point about extending CI/CD to data and models is spot on. Most teams nail the code testing part but completely underestimate how often input data drifts or model behavior shifts in production. What helped me was treating data validation as a first-class citizen in the pipeline, not just an afterthought, because catching bad data early saves you from debugging model failures downstream.
The trickiest part of Model CI that nobody talks about is the brittle middle ground between automated tests passing and the model actually being production-ready. You can validate data schemas and code quality all day, but if your retrained model has a 2% drop in precision on edge cases that your test set doesn't capture, automated CI just waves it through. Most teams I've worked with end up building a hybrid approval gate after the CI pipeline, which sort of defeats the automation promise.
Hi Avi..where do you make these diagrams? Thank you