Systems @Scale Spring 2022
Share

Scaling Data Ingestion for ML Training at Meta | Aarti Basant

AI models drive several Meta products like News Feed, Ads, IG Reels, language translation to name a few. Our ranking models consume massive datasets to continuously improve user experience on our platform. In this talk, we discuss our experience of building infrastructure to serve massive scale data to the 1000s of AI models driving our products. Further, we present AI training data pipeline workload characteristics and challenges in scaling these systems for industry-scale use cases. Inefficiencies in these pipelines result in expensive wasted GPU/accelerator training resources in our data centers. In this talk, we outline our experience in optimizing these data ingestion pipelines and our plans to continue innovation in this space.

Related Topics

Join the @Scale Mailing List and Get the Latest News & Event Info

Code of Conduct

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy