Using AI Models with WebAssembly Workshop

Overview

This workshop explores how Artificial Intelligence (AI) and WebAssembly (Wasm) combine to bring high-performance, portable machine learning directly into the browser. Traditionally, running AI models in the browser has faced major challenges — limited compute power, JavaScript’s performance overhead, and complex dependency management. WebAssembly changes that equation.

Through a series of guided, hands-on modules, you’ll learn how to:

• Build and run lightweight AI models using ONNX Runtime Web or TensorFlow.js.
• Optimize inference performance using Wasm backends.
• Integrate Rust-based modules for preprocessing and postprocessing.
• Visualize, benchmark, and deploy AI demos that run entirely on the client side — no servers required.

By the end of this workshop, you’ll understand how to design browser-based AI applications that are fast, secure, and scalable — whether you’re building an intelligent web dashboard, a creative AI tool, or an educational demo.

What You’ll Do

Each module blends theory with experimentation:

1. Understand why AI and Wasm belong together.
   Measure the performance difference between JavaScript and Wasm for math-heavy tasks.
2. Build and export a simple AI model.
   Use Python to train and save an ONNX model (e.g., linear regression or digit classifier).
3. Run inference in the browser.
   Load model weights, prepare tensors, and measure inference speed using Wasm.
4. Extend functionality with Rust.
   Compile custom logic — like normalization or activation functions — into WebAssembly.
5. Visualize performance.
   Plot inference results and monitor memory or thread usage in real time.
6. Deploy your AI demo.
   Bundle and publish your Wasm-backed AI app on Netlify or Vercel.

Learning Outcome

After completing this workshop, you will be able to:

• Explain how WebAssembly accelerates browser-based AI workloads.
• Build, export, and run pre-trained models efficiently in the browser.
• Integrate Wasm modules into existing web apps for AI inference and optimization.
• Deploy a fully working, interactive AI web demo — ready to share or showcase.