CML
An open-source CLI tool for implementing CI/CD workflows with a focus on MLOps, automating ML experiments and reporting.
What is CML?
Continuous Machine Learning (CML) is an open-source CLI tool that brings continuous integration and delivery (CI/CD) practices to machine learning projects. It automates key MLOps workflows such as model training, evaluation, and reporting directly within Git-based platforms like GitHub, GitLab, and Bitbucket. CML generates visual reports with metrics and plots automatically in pull requests, helping teams make data-driven decisions.
Machine learning engineers and data science teams who use Git-based platforms (GitHub, GitLab, Bitbucket) for version control and want to automate and standardize their ML experimentation, training, and reporting workflows. It is particularly useful for teams practicing MLOps and seeking to integrate rigorous engineering practices into their ML development.
Developers choose CML because it enables building custom ML platforms using existing Git and cloud infrastructure without requiring additional databases or services. Its unique selling point is the seamless automation of ML reporting within CI/CD pipelines, combined with native integration with DVC for data versioning and the ability to provision cloud runners (AWS, Azure, GCP, Kubernetes) for compute-intensive tasks directly from workflows.
Overview
♾️ CML - Continuous Machine Learning | CI/CD for ML
Use Cases
Best For
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