The Fifth Elephant 2024 Annual Conference (12th &13th July)
Maximising the Potential of Data — Discussions around data science, machine learning & AI
Jul 2024
8 Mon
9 Tue
10 Wed
11 Thu
12 Fri
13 Sat 09:00 AM – 06:05 PM IST
14 Sun
JOINAL AHMED
@infinitejock
Submitted Jun 21, 2024
In today’s interconnected world, deploying and accessing machine learning (ML) models efficiently poses major challenges. Traditional methods rely on cloud GPU clusters and constant internet connectivity. However, WebAssembly (Wasm) and WebGPU technologies are revolution
In today’s interconnected world, deploying and accessing machine learning (ML) models efficiently poses major challenges. Traditional methods rely on cloud GPU clusters and constant internet connectivity. However, WebAssembly (Wasm) and WebGPU technologies are revolutionizing this landscape. This talk explores leveraging Wasm and WebGPU to deploy small language models (SLMs) directly within web browsers, eliminating the need for extensive cloud GPU clusters and reducing reliance on constant internet access. We showcase practical examples and discuss how Wasm enables efficient cross-platform ML model execution while WebGPU optimizes parallel computation within browsers. Join us to discover how this fusion empowers developers and users alike with unprecedented ease and efficiency in browser-based ML, while reducing dependence on centralized cloud infrastructure and internet connectivity constraints.
The talk on leveraging WebAssembly and WebGPU for browser-based machine learning deployment offers numerous benefits to the ecosystem. It significantly reduces infrastructure costs by eliminating the need for large GPU clusters in the cloud, making ML accessible to a broader audience. Users experience enhanced accessibility and improved performance with seamless access to ML inference capabilities directly in their browsers. Developers benefit from simplified deployment processes and increased productivity, while scalability ensures adaptable solutions for varying workloads. Furthermore, deploying ML models within the browser enhances privacy and security by keeping data processing local, while WebAssembly’s cross-platform compatibility ensures accessibility across different devices and operating systems. By fostering innovation and empowering emerging markets with limited access to high-performance computing resources, this approach contributes to a more inclusive and sustainable ecosystem for browser-based machine learning deployment.
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