CI/CD for ML
Last Updated: May 29, 2026
Traditional CI/CD tests code, builds an artifact, and deploys it. ML systems have more ways to change.
The code changes, but so does the data, the trained model, and a long list of things around them: feature definitions, label policy, prompts, the retrieval index, serving thresholds. A production release process for ML has to validate all of them, not just the code.
CI/CD for ML is the discipline of promoting only tested data, models, and serving artifacts into production.