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CML Documentation

Continuous Machine Learning (CML) is an open-source tool for implementing continuous integration & delivery (CI/CD) in machine learning projects. Use it to automate parts of your development workflow, including model training and evaluation, comparing ML experiments across your project history, and monitoring changing datasets.

✅ Please join our community or use the support channels if you have any questions or need specific help. We are very responsive ⚡.

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✅ Contribute to CML on GitHub or help us improve this documentation 🙏.

CML principles

  • GitFlow for data science. Use GitLab or GitHub to manage ML experiments, track who trained ML models, or modified data and when. Codify data and models with DVC instead of pushing to a Git repo.

  • Auto reports for ML experiments. Auto-generate reports with metrics and plots in each Git Pull Request. Rigorous engineering practices help your team make informed, data-driven decisions.

  • No additional services. Build your own ML platform using just GitHub or GitLab and your favorite cloud providers: AWS, Azure, GCP, or Kubernetes. No databases, services or complex setup needed


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