ML Workspace

5.7easy start · #22 of 32
in Data Science Platforms

Free to practise onnot on record
Free trialnot on record
Well documentednot on record
Runs where you worknot on record

Runs on Linux, Mac, self-hosted, Web, Windows.

ML Workspace is ranked #22 of 32 in data science platforms on The Geeks Club. It runs on Linux, macOS, Self-hosted, Web, Windows.

Compared on data science platforms

Free plan: Yes
Hosted notebooks: Yes
Deployment options: self_hosted
Version control: Yes
Supported languages: Python; R (R flavor); Scala, Go, and others via additional kernels

Best ML Workspace alternatives

See all 20

Deepnote 0 of 4 in the starter kit Lesson 17.8 Datalore 0 of 4 in the starter kit Lesson 27.7 KNIME Analytics Platform 0 of 4 in the starter kit Lesson 37.7 Amazon SageMaker Autopilot 0 of 4 in the starter kit Lesson 47.5 Hex 0 of 4 in the starter kit Lesson 57.5 Paperspace Gradient 0 of 4 in the starter kit Lesson 67.4

The record

Product: ML Workspace is a self-deployed, web-based IDE for machine learning and data science.
Development tools: It includes browser-based Jupyter, JupyterLab, Visual Studio Code, and a Linux desktop GUI.
ML libraries: The main image comes preinstalled with data science libraries including TensorFlow, PyTorch, Keras, and scikit-learn.
Git: It includes Git tools such as a Jupyter extension for pushing notebooks, the Ungit web client, Jupytext, and nbdime.
Monitoring: It provides TensorBoard for training monitoring and Netdata and Glances for hardware monitoring.
Remote development: It can serve as a remote runtime for Jupyter, VS Code, PyCharm, Colab, and Atom Hydrogen, typically through passwordless SSH.
Deployment: The project provides Docker images and says they can be deployed on Mac, Linux, and Windows; Docker is required.
Security: The documentation describes token or basic authentication options and configurable SSL/HTTPS support.
Security checks: The maintainers say each minor release receives vulnerability and virus checks using Safety, ClamAV, Trivy, and Snyk via Docker Scan.
Resource requirements: The documentation says the workspace needs at least 2 CPUs and 500 MB of memory to run stably and be usable.
User model: The workspace is designed as a single-user development environment; the maintainers recommend ML Hub for multi-user deployments.
Support: The maintainers say they cannot provide individual support by email and direct users to public support channels; the page lists [email protected] for other requests.

Where it ranks on The Geeks Club

Best Data Science Platforms in 2026#22 of 32

Is ML Workspace yours?

Claim it for free: prove the domain, then correct facts, plans and screenshots. An editor reviews every change.

Claim it free Promote ML Workspace

Sources

github.com/ml-tooling/ml-workspace· checked 5 Oct 2026