# MLReef/mlreef

The collaboration workspace for Machine Learning

Repository: https://github.com/MLReef/mlreef
Canonical: https://ross.abutalabs.com/products/mlreef
Homepage: https://mlreef.com
Language: Kotlin
License: NOASSERTION
License Family: other
Topics: mlops, mlops-environment, artificial-intelligence, machine-learning, machine-learning-algorithms, deep-learning, deeplearning, models, tensorflow, pytorch, data-science, mxnet, reproducibility
Last push: 2022-11-01T18:53:56+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2235, "days_push": 1401, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1459, forks 304 (observed 2026-08-28T04:04:47.215631+00:00)

## What it is
MLReef is an open-source ML-Ops collaboration platform combining versioned data hosting, containerized code publishing, experiment tracking, and pipeline orchestration for machine learning projects. This GitHub mirror is no longer maintained; development moved to the project's main repository on GitLab.

## Use cases
- version and host training datasets with git and git-lfs
- track machine learning experiments and their results
- publish containerized ML scripts as reusable pipeline steps
- orchestrate ML training and data processing pipelines on kubernetes
- collaborate with a team on reproducible ML workflows
- manage datasets with processing and visualization history

## When to choose
- you need a self-hosted, git-based end-to-end ML-Ops platform
- your team wants dataset versioning plus experiment tracking in one place
- you want to run ML pipelines on kubernetes, cloud, or bare metal

## When to avoid
- you need actively maintained software - this GitHub repo is abandoned and development moved to GitLab
- you only need lightweight experiment tracking without a full platform
- you want a managed SaaS rather than self-hosting a complex multi-service system

## Facets
- artifact type: service
- maturity: abandoned
- function: machine-learning, workflow-automation, data-science, version-control, monitoring, deployment
- domain: machine-learning, data-science, artificial-intelligence, developer-tools, self-hosted
- platform: self-hosted, python
- tags: mlops, experiment-tracking, data-versioning, ml-pipelines, git-lfs, reproducibility, collaboration-platform, docker, kubernetes, web-server

## Member repositories
- MLReef/mlreef (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:47.215631+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 2026-08-30T04:35:26.253797+00:00, confidence not recorded.
  - readme: https://github.com/MLReef/mlreef (fetched 2026-08-28T04:04:47.215631+00:00, sha 4ced0bbf879f)
  - homepage: https://mlreef.com (fetched 2026-08-29T11:44:14.557202+00:00, sha 348ee62ac78d)
- Data as of 2026-08-30T08:39:29.467469+00:00.