Links
- CodeCrafters (partner): https://tej.as/codecrafters
- WebAssembly on Kubernetes: https://www.cncf.io/blog/2024/03/12/webassembly-on-kubernetes-from-containers-to-wasm-part-01/
- Shivay on X: https://x.com/howdevelop
- Tejas on X: https://x.com/tejaskumar_
Summary
In this podcast episode, Shivay Lamba and I discuss the integration of WebAssembly with AI and machine learning, exploring its implications for developers. We dive into the benefits of running machine learning models in the browser, the significance of edge computing, and the performance advantages of WebAssembly over traditional serverless architectures. The conversation also touches on emerging hardware solutions for AI inference and the importance of accessibility in software development. Shivay shares insights on how developers can leverage these technologies to build efficient and privacy-focused applications.
Chapters
00:00 Shivay Lamba
03:02 Introduction and Background
06:02 WebAssembly and AI Integration
08:47 Machine Learning on the Edge
11:43 Privacy and Data Security in AI
15:00 Quantization and Model Optimization
17:52 Tools for Running AI Models in the Browser
32:13 Understanding TensorFlow.js and Its Architecture
37:58 Custom Operations and Model Compatibility
41:56 Overcoming Limitations in JavaScript ML Workloads
46:00 Demos and Practical Applications of TensorFlow.js
54:22 Server-Side AI Inference with WebAssembly
01:02:42 Building AI Inference APIs with WebAssembly
01:04:39 WebAssembly and Machine Learning Inference
01:10:56 Summarizing the Benefits of WebAssembly for Developers
01:15:43 Learning Curve for Developers in Machine Learning
01:21:10 Hardware Considerations for WebAssembly and AI
01:27:35 Comparing Inference Speeds of AI Models
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