Continuous Machine Learning (CML) is an open-source CLI tool for implementing continuous integration & delivery (CI/CD) with a focus on MLOps. Use it to automate development workflows — including machine provisioning, model training and evaluation, comparing ML experiments across project history, and monitoring changing datasets. CML can help train and evaluate models — and then generate a visual report with results and metrics — automatically on every pull request.
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 GitLab, Bitbucket, or GitHub. Optionally, use cloud storage as well as either self-hosted or cloud runners (such as AWS EC2 or Azure). No databases, services or complex setup needed.