Explore edge AI design strategies for client-to-client e-commerce applications in this 35-minute talk by Yuji Oshima from the Linux Foundation. Delve into the implementation of client-side machine learning in Mercari, examining its advantages and development process. Discover four key production considerations, compare server-side and client-side approaches, and analyze the Mercari Lens model architecture. Learn about inference on streaming media, pre/post-processing techniques, and the use of MediaPipe. Investigate process pipeline definition, inference optimization, and validation methods. Discuss the challenges of web-based tools and explore architecture for native applications, including the potential for web migration with native-like performance. Gain valuable insights into cutting-edge AI implementation for e-commerce platforms.
Edge AI Design Strategy for C2C E-Commerce Applications
Overview
Syllabus
Intro
Agenda
Background replacement on Google Meet
Client-side ML in Mercari
Why Client-side ML
How to develop Client-side ML
For more advanced use cases
Four key points concerning production
Is the Server-side enough?
In the case of Mercari Lens
Model architecture
Inference on Streaming Media
Pre/Post Process
Introduce MediaPipe
Define a process pipeline
Inference with the pipeline
Optimize the pipeline
Validation
Tools for web
Unfortunately, NO
Architecture for native application
We could migrate to web!
Native like performance on web
Conclusion
Taught by
Linux Foundation
