How is CI/CD for ML models different from CI/CD for reg...

Standard software CI/CD asks one question — does the code work? — and a passing test suite is sufficient to ship. ML CI/CD has to ask a second, harder question: is the artifact this pipeline just produced actually better than what’s already serving traffic? Code can be perfectly correct and still train a worse model, because model quality depends on data and hyperparameters as much as logic.

That’s why a production ML pipeline adds a data-validation stage before training even starts — schema and distribution checks that stop garbage data from producing a garbage model — and, non-negotiably, a champion-versus-challenger evaluation gate at the end: the new model only deploys if it beats the currently deployed model on the metrics that matter, otherwise the pipeline rejects it and keeps the champion running. Determinism also needs handling explicitly, since the same code and data can still produce a different model run to run without a fixed seed.

A team that skips the comparison gate and auto-deploys anything that “finishes training successfully” eventually ships a regression with a fully green pipeline — the training job didn’t error, it just trained on a worse hyperparameter draw. The gate is what makes automated retraining safe to leave unattended.