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Tech riderRev. 5 Oct 2026

ML Workspace

Ch 057.4of 32 Data Science Platforms
  1. 1Runs onLinux, Mac, self-hosted, Web, Windows
  2. 2CostsFree plan
  3. 3Hosted notebooksYes
  4. 4Deployment optionsself hosted
  5. 5Version controlYes
  6. 6Supported languagesPython; R (R flavor); Scala, Go, and others via additional kernels
6 lines stated Written from the maker's own pages: mltooling.org, github.com
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The ML Workspace homepage

Overview

ML Workspace is ranked #5 of 32 in data science platforms on Specifiction. 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
Yesmltooling.org
Hosted notebooks
Yesmltooling.org
Deployment options
self_hostedmltooling.org
Version control
Yesmltooling.org
Supported languages
Python; R (R flavor); Scala, Go, and others via additional kernelsmltooling.org

Facts

Product
ML Workspace is a self-deployed, web-based IDE for machine learning and data science.github.com · 5 Oct 2026
Development tools
It includes browser-based Jupyter, JupyterLab, Visual Studio Code, and a Linux desktop GUI.github.com · 5 Oct 2026
ML libraries
The main image comes preinstalled with data science libraries including TensorFlow, PyTorch, Keras, and scikit-learn.github.com · 5 Oct 2026
Git
It includes Git tools such as a Jupyter extension for pushing notebooks, the Ungit web client, Jupytext, and nbdime.github.com · 5 Oct 2026
Monitoring
It provides TensorBoard for training monitoring and Netdata and Glances for hardware monitoring.github.com · 5 Oct 2026
Remote development
It can serve as a remote runtime for Jupyter, VS Code, PyCharm, Colab, and Atom Hydrogen, typically through passwordless SSH.github.com · 5 Oct 2026
Deployment
The project provides Docker images and says they can be deployed on Mac, Linux, and Windows; Docker is required.github.com · 5 Oct 2026
Security
The documentation describes token or basic authentication options and configurable SSL/HTTPS support.github.com · 5 Oct 2026
Security checks
The maintainers say each minor release receives vulnerability and virus checks using Safety, ClamAV, Trivy, and Snyk via Docker Scan.github.com · 5 Oct 2026
Resource requirements
The documentation says the workspace needs at least 2 CPUs and 500 MB of memory to run stably and be usable.github.com · 5 Oct 2026
User model
The workspace is designed as a single-user development environment; the maintainers recommend ML Hub for multi-user deployments.github.com · 5 Oct 2026
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.github.com · 5 Oct 2026
Purpose
ML Workspace is an all-in-one web-based IDE specialized for machine learning and data science.github.com · 5 Oct 2026
Included IDEs
It provides browser-accessible Jupyter, JupyterLab, Visual Studio Code, and a Linux desktop GUI.github.com · 5 Oct 2026
Libraries
The main image comes preloaded with data science libraries including TensorFlow, PyTorch, Keras, and scikit-learn.github.com · 5 Oct 2026
Integrations
Listed tools and integrations include Git, TensorBoard, Netdata, Jupyter, JupyterLab, and Visual Studio Code.github.com · 5 Oct 2026
Remote access
The workspace can be accessed through a browser, SSH, or VNC, and supports remote Jupyter kernels and VS Code development over SSH.github.com · 5 Oct 2026
Flavors
Available image flavors include minimal, R, Spark, and GPU variants.github.com · 5 Oct 2026
GPU requirements
The GPU flavor requires compatible Nvidia drivers and supports CUDA 11.2 according to the project documentation.github.com · 5 Oct 2026
Authentication
The project recommends enabling Jupyter token authentication or Nginx basic authentication for access to preinstalled tools through the main workspace port.github.com · 5 Oct 2026
Encryption
SSL/HTTPS can be enabled with supplied certificates or generated self-signed certificates.github.com · 5 Oct 2026
Resource needs
The documentation says the workspace requires at least 2 CPUs and 500MB to run stably and be usable.github.com · 5 Oct 2026
Security limitation
The documentation says using a non-root user is not currently supported and notes the general container escape risk associated with root privileges.github.com · 5 Oct 2026

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Where it ranks on Specifiction

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