ML Workspace

At a glance

• Editor score
  6.6 / 10
• Pricing
  Free plan
• Best for
  Solo users who want a self-hosted notebook workspace
• Free plan
  Yes
• Paid from
  Free
• Hosted notebooks
  Yes
• Facts checked
  23 Sep 2026

• Where it wins

  • Jupyter, JupyterLab, and VS Code are available in the browser.
  • Includes data science libraries, Git, and TensorBoard monitoring.
  • Offers SSH access, remote development, and Python jobs.

• Where it doesn't

  • Designed as a single-user environment, not a multi-user team workspace.
  • Self-hosting means deployment is on your own machine or server.
  • Support is community-based.

Our verdict on ML Workspace

ML Workspace brings browser-accessible Jupyter, JupyterLab, and Visual Studio Code into a Docker-deployable environment for machine learning and data science. It is aimed at solo users who want notebooks and development tools on their own machine or server rather than a shared hosted service. The workspace includes preinstalled Python data science libraries such as TensorFlow, PyTorch, Keras, and scikit-learn, alongside a Linux desktop GUI accessible through a browser. R is available through an R flavor, while Scala, Go, and other languages can be used through additional kernels.

Its breadth is a draw for an individual workflow: notebook and code editing sit alongside Git integration, SSH access for remote development, and the option to run Python code as jobs. TensorBoard and hardware monitoring cover model training and system visibility, with Netdata also listed among integrations. This mix makes it more than a notebook interface, but it remains a self-hosted development environment rather than a managed platform. Users should expect to deploy and maintain it on their own infrastructure.

The project is open source under Apache-2.0, and its free status follows from that open-source release. The published project description presents the workspace as single-user and points to a separate ML Hub project for multi-user deployments. Community support is the listed channel, so teams that need a shared workspace or a dedicated support path should consider alternatives. For solo practitioners comfortable with self-hosting, ML Workspace consolidates familiar data science tools in one browser-accessible workspace; users seeking managed hosting or collaboration across a team are a poorer fit.

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ML Workspace pricing

ML Workspace fact sheet

ML Workspace integrations

ML Workspace lists 6 integrations on its own site.

• Jupyter
• JupyterLab
• Visual Studio Code
• TensorBoard
• Netdata
• Git

Alternatives to ML Workspace

• SAS ViyaA broad analytics and model-operations platform with flexible deployment and sales-led pricing.9.3
• Amazon SageMaker AIA broad AWS-hosted ML workflow for teams developing and operating models.9.2
• Azure Machine LearningA broad Azure ML lifecycle platform, with compute costs billed separately.9.1

See all ML Workspace alternatives →

Featured on iTechGuides

ML Workspace is listed in our Data Science Platforms directory. Add the badge to your site — it links back to this page.

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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

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Last updated 7 Oct 2026 · How we research and update