# iterative/cml

♾️ CML - Continuous Machine Learning | CI/CD for ML

Repository: https://github.com/iterative/cml
Canonical: https://ross.abutalabs.com/products/cml
Homepage: http://cml.dev
Language: JavaScript
License: Apache-2.0
License Family: permissive
Topics: machine-learning, data-science, cicd, ci-cd, github-actions, gitlab-ci, developer-tools, continuous-integration, continuous-delivery, ci, bitbucket-pipelines, hacktoberfest, cli
Last push: 2025-06-02T04:40:57+00:00

## Health v2 (maintenance only)
Score: 45/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 24, release rhythm 40, longevity 100
- inputs: {"age_days": 2380, "days_push": 457, "days_rel": 678, "gap_med": 0, "n_releases_24m": 2}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4187, forks 344 (observed 2026-08-28T04:08:38.677389+00:00)

## What it is
CML (Continuous Machine Learning) is an open-source CLI tool that brings CI/CD practices to machine learning projects on GitHub, GitLab, and Bitbucket. It automates model training and evaluation, provisions cloud or self-hosted runners, and posts visual reports with metrics and plots on every pull request.

## Use cases
- add ci/cd to machine learning projects
- auto-generate ml experiment reports on pull requests
- train and evaluate models in github actions or gitlab ci
- compare ml experiments across project history
- provision self-hosted or cloud runners for ml training
- monitor changing datasets in ci pipelines
- build an ml platform without extra services

## When to choose
- you want MLOps automation using only your existing Git hosting platform
- you need automatic metrics and plot reports in pull requests
- you use DVC and want data and models in your CI pipeline
- you want to avoid running a separate ML platform service

## When to avoid
- you need a full managed ML platform with UI-based experiment tracking
- your project is not hosted on GitHub, GitLab, or Bitbucket
- you don't use CI/CD workflows for your ML development

## Facets
- artifact type: cli-tool
- maturity: active
- function: ci-cd, cli, workflow-automation, machine-learning, data-visualization
- domain: machine-learning, data-science, developer-tools
- platform: cli, cloud, self-hosted
- tags: mlops, github-actions, gitlab-ci, bitbucket-pipelines, dvc, continuous-integration, continuous-delivery, experiment-tracking, iterative-ai, reporting, devops, automation, nodejs, docker

## Member repositories
- iterative/cml (main) score 45

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:38.677389+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:22:37.380127+00:00, confidence not recorded.
  - readme: https://github.com/iterative/cml (fetched 2026-08-28T04:08:38.677389+00:00, sha 0d59eb6a34c4)
  - homepage: http://cml.dev (fetched 2026-08-29T09:13:24.488310+00:00, sha a411e2b436bc)
  - site_page: https://cml.dev/doc/install (fetched 2026-08-29T09:13:24.499368+00:00, sha 7ea3430400c2)
  - site_page: https://cml.dev/doc (fetched 2026-08-29T09:13:24.497644+00:00, sha 3b1108ba7bd7)
- Data as of 2026-08-30T08:39:29.467469+00:00.