Continuous Machine Learning (CML) is an open-source library 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.
On every pull request, CML helps you automatically train and evaluate models, then generates a visual report with results and metrics. Above, an example report for a neural style transfer model.
We built CML with these principles in mind:
- 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 services: AWS, Azure, GCP, or Kubernetes. No databases, services or complex setup needed.
Need help? Just want to chat about continuous integration for ML? Visit our Discord channel!
🌟 Check out our YouTube video series for hands-on MLOps tutorials using CML! 🌟