Hexagon-ML vs ML Workspace
Hexagon-ML
hexagon-ml.comML Workspace
mltooling.orgSide by side
| Figure | Hexagon-ML | ML Workspace |
|---|---|---|
| Price from | — | Free |
| Free plan | — | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | Yes |
| Windows | — | Yes |
| macOS | — | Yes |
| Linux | — | Yes |
| Self-hosted | — | Yes |
| Plans published | 0 | 1 |
| Spec score · Data Science Platforms | 6.8 (#20) | 8.3 (#7) |
| Deployment | The site describes scalable Docker-based solutions and Active Directory integration. | The project provides Docker images and says they can be deployed on Mac, Linux, and Windows; Docker is required. |
| Development tools | The platform lists Python, R, and Julia development tools. | It includes browser-based Jupyter, JupyterLab, Visual Studio Code, and a Linux desktop GUI. |
| Product | Hexagon-ML describes its offering as an enterprise-ready platform for model insights, model development, and collaborative data science challenges. | ML Workspace is a self-deployed, web-based IDE for machine learning and data science. |
| Security | A company job description says Hexagon-ML aims to provide enterprise-level security and consumer-level flexibility; it does not specify a certification or compliance standard there. | The documentation describes token or basic authentication options and configurable SSL/HTTPS support. |
| Support | The company’s product articles list [email protected] for questions. | 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. |
Plans
In their own words
Hexagon-ML
Hexagon-ML has no write-up on Specifiction yet; its figures are above.
ML Workspace
ML Workspace is a self-deployed, web-based development environment for machine learning and data science. It brings browser-accessible Jupyter, JupyterLab, Visual Studio Code, and a Linux desktop GUI together, with libraries such as TensorFlow, PyTorch, Keras, and scikit-learn in its main image. Git-related tools include Ungit, Jupytext, nbdime, and a Jupyter extension for pushing notebooks. TensorBoard supports training monitoring, while Netdata and Glances cover hardware monitoring. The workspace can provide a remote runtime for Jupyter, VS Code, PyCharm, Colab, and Atom Hydrogen, typically through passwordless SSH, and can be accessed through a browser, SSH, or VNC. Image options include minimal, R, Spark, and GPU variants; the GPU flavor requires compatible Nvidia drivers and supports CUDA 11.2. Docker is required, with images deployable on Mac, Linux, and Windows. The project describes token or basic authentication and configurable SSL/HTTPS. It is designed for one user, and at least 2 CPUs and 500 MB of memory are recommended for stable use. The free plan costs nothing.