Cross-Platform ML.NET Model Builder Extension for Visual Studio Code #3091

Copy-pasting an issue from @xsa-dev dotnet/machinelearning#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 ml CLI (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 Interactive notebooks for visualization.

🧠 Developer Experience

• Works on Windows, macOS, Linux, and Codespaces.
• Uses modern VS Code APIs (WebView2, ES modules).
• Built with esbuild or vite for 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:

1. Migrate legacy codebase to modern VS Code API.
2. Integrate ML.NET CLI 3.x+.
3. Build a visual training wizard (dataset → train → evaluate → export).
4. Add ONNX / TorchSharp export & inference playground.
5. Publish and maintain cross-platform builds.