What is CML used for?

CML (Continuous Machine Learning) is a free, Apache-licensed CLI tool that brings CI/CD practices to machine learning, automating training, evaluation and reporting. It is a good fit for CI/CD pipelines for machine learning models and automated experiment comparison on pull requests.

Yes. CML is open source under the Apache-2.0 licence. Its source code is on GitHub at iterative/cml and is written mainly in JavaScript.

Yes. CML is open source, so the software itself is free to use.

What is CML an alternative to?

CML is an open-source alternative to Valohai. Other open-source alternatives to Valohai include labml and MLflow.

Is CML actively maintained?

The most recent commit to CML was on 2 June 2025, and the latest release is v0.20.6, published on 24 October 2024. The project has 4.2k stars on GitHub.

CML: open-source alternative to Valohai

About CML

CML, or Continuous Machine Learning, is an open-source command-line tool for applying CI/CD practices to machine learning work, with a focus on MLOps. It is aimed at data science teams who want to automate workflows such as provisioning machines, training and evaluating models, and comparing experiments across a project's history, using the Git-based tools they already rely on.

It can train and evaluate models automatically on every pull request and generate a visual report of results and metrics directly in that pull request, following a 'GitFlow for data science' approach where GitLab, GitHub or Bitbucket track who changed models or data and when, often paired with DVC to version data and models outside of Git itself. CML needs no additional backend services, databases, or complex setup, working on top of existing GitHub Actions, GitLab CI or Bitbucket Pipelines infrastructure with either self-hosted or cloud runners such as AWS EC2 or Azure.

CML is written in JavaScript and released under the Apache-2.0 license. The project provides a Discord community and a YouTube tutorial series for hands-on MLOps guidance, alongside documentation covering setup for each supported Git platform.

Key features

Automated model training and evaluation in CI
Auto-generated pull request reports with metrics
Works with GitHub Actions, GitLab CI, Bitbucket Pipelines
Integrates with DVC for data and model versioning
No extra databases or backend services needed
Self-hosted or cloud runner support

Good fit for

→CI/CD pipelines for machine learning models
→Automated experiment comparison on pull requests

Built with: JavaScript
Tags: mlops; ci-cd; machine-learning; github-actions; gitlab-ci; data-science; continuous-integration

CML: questions and answers

What is CML used for?: CML (Continuous Machine Learning) is a free, Apache-licensed CLI tool that brings CI/CD practices to machine learning, automating training, evaluation and reporting. It is a good fit for CI/CD pipelines for machine learning models and automated experiment comparison on pull requests.
Is CML open source?: Yes. CML is open source under the Apache-2.0 licence. Its source code is on GitHub at iterative/cml and is written mainly in JavaScript.
Is CML free?: Yes. CML is open source, so the software itself is free to use.
What is CML an alternative to?: CML is an open-source alternative to Valohai. Other open-source alternatives to Valohai include labml and MLflow.
Is CML actively maintained?: The most recent commit to CML was on 2 June 2025, and the latest release is v0.20.6, published on 24 October 2024. The project has 4.2k stars on GitHub.