Cross-Platform ML.NET Model Builder Extension for Visual Studio Code
Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 25/100
- Issue type
- Feature
- Clarity
- Needs clarification
- Activity status
- Active
- Tech stack
- vscode
- Domain
- developer-experience, machine-learning, tooling
Research direction
Review the linked MLBuilderVSCode-Source prototype and the repository entry points first, then compare them with the proposed dotnet ml integration and VS Code WebView workflow. The roadmap defines done as a cross-platform extension covering dataset selection, training and evaluation, and model export, but the implementation starting point and scope still need agreement.
Written by the indexing model from the issue text.
Description
Copy-pasting an issue from @xsa-dev https://github.com/dotnet/machinelearning/issues/7545
🚀 Feature Proposal: Cross-Platform ML.NET Model Builder Extension for Visual Studio Code
Is your feature request related to a problem? Please describe.
Currently, ML.NET Model Builder exists only as a Visual Studio for Windows component.
Developers who use VS Code, macOS, Linux, or GitHub Codespaces have no accessible, GUI-based way to build or train ML.NET models.
This leaves a large part of the .NET developer ecosystem — especially those on non-Windows environments — without first-class tooling for ML.NET.
Describe the solution you'd like
Develop or officially support a VS Code extension (“ML.NET Model Builder for VS Code”) that provides a modern, lightweight, and cross-platform workflow powered by the ML.NET CLI.
🔧 Core Capabilities
- Integration with
dotnet mlCLI (ML.NET 3.x+). - Scenario wizard: classification, regression, recommendation, anomaly detection.
- Dataset selection (CSV, TSV, Parquet).
- Label & feature selection, ignore columns, training time configuration.
- Live training progress and evaluation metrics in VS Code WebView.
- Export models to ONNX or TorchSharp formats.
- Optional GPU training (CUDA / OneAPI).
- Integration with
.NET Interactivenotebooks for visualization.
🧠 Developer Experience
- Works on Windows, macOS, Linux, and Codespaces.
- Uses modern VS Code APIs (WebView2, ES modules).
- Built with
esbuildorvitefor fast packaging. - Published to Visual Studio Marketplace and OpenVSX.
- Potential starting point: franzsilva/MLBuilderVSCode-Source (early prototype).
Describe alternatives you've considered
- Using ML.NET CLI directly (functional but non-visual).
- Running Visual Studio Model Builder in Windows VMs or containers (heavy and impractical).
- Using Jupyter notebooks with ML.NET (not native to .NET developers’ workflow).
Additional context
A cross-platform Model Builder aligns with the .NET Everywhere strategy and lowers the entry barrier for ML.NET adoption.
It would make ML.NET approachable for students, researchers, and engineers working in containerized or non-Windows environments.
It could also serve as a bridge to AutoML.NET and ONNX Runtime for inference workloads.
Example roadmap:
- Migrate legacy codebase to modern VS Code API.
- Integrate ML.NET CLI 3.x+.
- Build a visual training wizard (dataset → train → evaluate → export).
- Add ONNX / TorchSharp export & inference playground.
- Publish and maintain cross-platform builds.
- Dominant language
- Dockerfile
- Stars
- 285
- Forks
- 66
- PR merge metrics
- No merged PRs in 30d
Getting set up
This project ships no dev container, Dockerfile or contributing guide, so setting up is up to you: start from its README, and see our first-contribution guide for the general steps.
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
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