TensorFlow & AI Frameworks
Wednesday, October 27, 2021
FEATURED TALK: (AI): Responsible AI into Practice - Deliver Trust in Artificial Intelligence SolutionJoin on Hopin
AI has been a key driver in innovation in every industry Organizations have ramped up their effort on leveraging AI to gain a competitive advantage. However, AI solution comes with its own challenges and risk, particularly in regulated industries. There have been numerous instances when AI introduced bias. Organizations must use a balanced approach to accelerating the adoption of AI and prioritize AI governance to ensure trust in the AI system. While AI regulation landscape is still evolving, now is the time for organizations to start taking steps to understand and mitigate AI risks. Responsible AI framework provides guidelines around AI governance for building fair, transparent, ethical, and accountable AI solutions. In this session you will learn about how organizations can follow Responsible AI guidelines and operationalize trust in AI solutions by incorporating AI governance throughout the AI/ML life cycle.
This presentation will cover options to run Tensorflow model inference on WebAssembly. We will start from the unique challenges of deploying AI inference models in production, and how Rust + WebAssembly could help. Using a Mobilenet image classification task as an example, we will discuss the pros and cons of the plain JS approach, Tensorflow.js, pure Rust crates for Tensorflow compiled to Wasm, and WASI-like Tensorflow Wasm extensions that run on specialized inference chips. We will go through the journey of 60,000x performance gain over different WebAssembly approaches. We will also discuss what’s the future for WebAssembly-based AI on the edge cloud.