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CML
CI/CD for machine learning — continuous integration and delivery for ML
Apache-2.0
ABOUT
Machine learning projects lack standard CI/CD practices — model training is not automatically tested, experiment results are not versioned, and deployment is a manual process. CML solves this by bringing DevOps to ML, letting teams define automated pipelines that train models on GPU runners, compare metrics across commits, generate reports as CI artifacts, and deploy models — all within their existing GitHub Actions or GitLab CI setup.
INSTALL
npm install -g @dvcorg/cmlINTEGRATION GUIDE
1. Automatically train and evaluate ML models on every git push with results reported as CI comments
2. Create a CI pipeline that tests model accuracy against a baseline and blocks merges if accuracy drops
3. Build an end-to-end ML deployment pipeline that trains, packages, and deploys models to production
4. Implement experiment tracking that compares model metrics across git commits with visual reports
5. Set up a reproducible ML workflow where infrastructure and dependencies are defined in code
TAGS
ci-cdmlopsdevopsautomationgithub-actionsgitlab-citestingpython