Microsoft notebooks provides integrated Jupyter notebook experiences for Python and data science.
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15 alternativesML Workspace
All-in-one web-based IDE specialized for machine learning and data science.
About ML Workspace
ML Workspace is a web-based development environment for machine learning and data science that runs in Docker on the user's own machine. It includes Jupyter, JupyterLab and Visual Studio Code, with pre-installed libraries such as TensorFlow, PyTorch, Keras and Scikit-learn and tools such as TensorBoard.
It requires Docker to be installed and is started with a docker run command, then accessed through localhost port 8080 or a machine IP and exposed port. The first image download can take a few minutes depending on internet speed. It also provides a Linux desktop GUI, Git integration for notebooks, and hardware and training monitoring through TensorBoard and Netdata.
Key features
- Includes Jupyter, JupyterLab and Visual Studio Code web IDEs
- Pre-installed TensorFlow, PyTorch, Keras and Scikit-learn libraries
- Full Linux desktop GUI accessible through web browser
- Git integration optimized for notebooks
- Hardware and training monitoring with TensorBoard and Netdata
- Access via Web, SSH, or VNC under a single port
Best for: Developers who want to build machine learning solutions on their own machines using Docker.
Good for
- Deploys on Mac, Linux and Windows via Docker
- Combines multiple IDEs, libraries and monitoring tools in one container
- Configurable with environment variables and workspace folder mounting
Watch out for
- Requires Docker to be installed before use
- First image download can take a few minutes depending on internet speed
- Needs 512MB shared memory setting to prevent unexpected crashes
ML Workspace at a glance
- Category
- Development › Code Editors & IDEs
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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ML Workspace FAQ
What do I need to run ML Workspace?
Docker must be installed on the machine before deploying the workspace.
How do I start a single instance?
Run docker run -p 8080:8080 mltooling/ml-workspace:0.13.2, then open http://localhost:8080 after the image is pulled.
How do I access it on another machine or port?
Use the machine's IP or DNS name and the exposed port instead of localhost.
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