CML
Continuous Integration for ML projects with GitHub Actions & GitLab CI.
Pricing
Free tier
Flat rate
Adoption
↘CoolingLicense
Open Source
Data freshness
Verified · Jul 12, 2026Overview
What is CML?
CML is a library that enables continuous integration in machine learning projects by leveraging GitHub Actions and GitLab CI. It automates the training, evaluation of models, and generates visual reports directly within pull/merge requests.
Key differentiator
“CML stands out by providing a framework-agnostic solution for automating ML model evaluation and reporting directly within CI/CD workflows, making it ideal for teams that prioritize consistency across different development stages.”
Capability profile
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
API requires Python-specific patterns, TypeScript SDK is community-maintained
v0.1 to v0.2 migration required rewriting chain definitions
Primary integration is GitHub Actions and GitLab CI, other CI systems are not officially supported
CI environments may have resource constraints that limit the speed and efficiency of model training and evaluation
Fit analysis
Who is it for?
✓ Best for
Teams that need to automate the evaluation of machine learning models in CI/CD pipelines
Projects requiring automated generation of visual reports for model performance metrics
Developers working with multiple ML frameworks and languages who want a consistent integration solution
✕ Not a fit for
Projects that do not use GitHub Actions or GitLab CI as their CI/CD platform
Teams looking for a fully managed service without the need to set up CI pipelines manually
Cost structure
Pricing
Free Tier
Available
Open source — free to use
Starts at
$0
Model
Flat rate
Enterprise
None
Performance benchmarks
How Fast Is It?
Ecosystem
Relationships
Alternatives
Works well with
Next step
Get Started with CML
Step-by-step setup guide with code examples and common gotchas.