Solution Study
Tuesday, July 01
10:00 AM - 10:30 AM
Live in San Francisco
Less Details
The automotive industry heavily relies on AI platforms for model training and serving. However, the fragmented data tool landscape and legacy platforms hinder the scalability and efficiency of AI platforms. This talk addresses the challenges faced by data platform teams, such as slow data loading using PyTorch and Ray, low GPU utilization, and high costs associated with retrieving data from cloud object storage. In this session, Bin Fan and Tarik Bennett will share their insights and solutions from production experiences, demonstrating how Alluxio helps automotive and smart vehicle companies maximize their AI infrastructure.
Bin Fan is the VP of Technology at Alluxio. Prior to joining Alluxio as a founding engineer, he worked for Google to build the next-generation storage infrastructure. Bin received his PhD in computer science from Carnegie Mellon University on the design and implementation of distributed systems.
The Pop in Your Job – What drives you? Why do you love your job?
I’m passionate about solving complex technical challenges that have a real-world impact. I love working on distributed systems and storage infrastructure because they form the critical foundation that powers all the incredible applications, from the Hadoop era to the GenAI era.