Ross ROSS = Recommend OSS · open-source software intelligence for agents

ml-tooling/ml-workspace

🛠 All-in-one web-based IDE specialized for machine learning and data science. observed · 2026-08-28

github.com/ml-tooling/ml-workspace · homepage · Jupyter Notebook · Apache-2.0 (permissive) observed · 2026-08-28

Health v2 · maintenance only

23/100

  • Activity 0
  • Release rhythm 8
  • Longevity 100
How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.

  • gap_med: n/a
  • age_days: 2655
  • days_rel: n/a
  • days_push: 768
  • n_releases_24m: 0

Full methodology

Adoption not part of the score

3544 stars · 458 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

ML Workspace is an all-in-one web-based IDE Docker image specialized for machine learning and data science. It bundles Jupyter, JupyterLab, VS Code, a Linux desktop GUI, and popular data science libraries (TensorFlow, PyTorch, scikit-learn) into a single deployable container.

Use cases

  • spin up a preconfigured jupyter environment for machine learning
  • run a remote jupyter kernel or vscode server over ssh
  • access a full linux desktop with data science tools from a browser
  • train pytorch or tensorflow models with gpu support in docker
  • set up a reproducible data science workspace on kubernetes
  • monitor training runs with tensorboard and netdata

When to choose

  • you want a batteries-included ML development environment deployable in minutes via Docker
  • you need browser-based access to Jupyter, VS Code, and a Linux desktop from one port
  • you want preinstalled data science libraries without local setup hassle

When to avoid

  • you need a lightweight minimal image or custom toolchain
  • you require actively developed features - the project's latest release is mid-2024 and appears in maintenance
  • you prefer native local IDEs over containerized web-based environments

Facets

application · maturity maintenance

developer-tools data-science machine-learning deep-learning data-visualization nlp machine-learning data-science developer-tools deep-learning windows self-hosted jupyter vscode web-ide tensorflow pytorch gpu remote-kernel ssh vnc anaconda docker linux macos web-server kubernetes

2 sources

Member repositories

RepositoryRoleHealth v2
ml-tooling/ml-workspacemain23

For agents

markdown · JSON · MCP: product_card(name="ml-tooling/ml-workspace")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem