Tuesday April 22, 11am PT

Coupang is a leading e-commerce company in South Korea, with over 50,000 employees and $20+ billion in annual revenue. Coupang's AI platform team builds and manages a large-scale AI platform in AWS for machine learning engineers to train models that enhance and customize product search results and product recommendations for its 100+ million customers.

As the search and recommendation models evolve, optimizing the underlying infrastructure for AI/ML workloads is essential for the e-commerce business. Coupang's platform team actively sought to improve their model training pipeline to boost machine learning engineers' productivity, publish models to production faster, and reduce operational costs. 

Coupang focused on addressing several key areas: 

1. Shortening data preparation and model training time
2. Improving GPU utilization in training clusters in different regions
3. Reducing S3 API and egress costs incurred from copying large training datasets across regions
4. Simplifying the operational complexity of storage system management

In this tech talk, Hyun Jung Baek, Staff Backend Engineer at Coupang, will share best practices for leveraging distributed cache to power search and recommendation model training infrastructure.

Hyun will discuss:

• How Coupang builds a world-class large-scale AI platform for machine learning engineers to deliver better search and recommendation models
• How adding distributed caching to their multi-region AI infrastructure improves GPU utilization, accelerates end-to-end training time, and significantly reduces cross-region data transfer costs.
• How to simplify platform operations and to easily deploy the same architecture to new GPU clusters.

About the Speaker

Hyun Jung Baek is a Staff Backend Engineer at Coupang.

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