ML Workspace

Web · Windows · Mac · Linux · Self-hosted

Freedom report

Three barsScore 6.6

Free tierA free tier is on its own pricing page
Open codeNo open-source code on record
Runs widely4 of 6 device platforms
DocumentedPlans, terms and facts published

ML Workspace is ranked #3 of 32 in data science platforms on Freedom251. It runs on Linux, macOS, Self-hosted, Web, Windows. There is a free plan.

ML Workspace plans and pricing

All plans

ML Workspace Free Single-user development environment · requires Docker · at least 2 CPUs and 500MB recommended github.com · 5 Oct 2026

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

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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.
Purpose: ML Workspace is an all-in-one web-based IDE specialized for machine learning and data science.
Included IDEs: It provides browser-accessible Jupyter, JupyterLab, Visual Studio Code, and a Linux desktop GUI.
Price from: Free

Where it ranks on Freedom251

Best Data Science Platforms in 2026#3 of 32

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Sources

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