Deep Recommender Systems at Facebook feat. Carole-Jean Wu | Stanford MLSys Seminar Episode 24

Episode 24 of the Stanford MLSys Seminar Series!
Designing AI systems for deep learning recommendation and beyond
Speaker: Carole-Jean Wu
Abstract:
The past decade has witnessed a 300,000 times increase in the amount of compute for AI. The latest natural language processing model is fueled with over trillion parameters while the memory need of neural recommendation and ranking models has grown from hundreds of gigabyte to the terabyte scale. This talk introduces the underinvested deep learning personalization and recommendation systems in the overall research community. The training of state-of-the-art industry-scale personalization and recommendation models consumes the highest number of compute cycles among all deep learning use cases at Facebook. For AI inference, recommendation use cases consume even higher compute cycles of 80%. What are the key system challenges faced by industry-scale neural personalization and recommendation models? This talk will highlight recent advances on AI system development for deep learning recommendation and the implications on infrastructure optimization opportunities across the machine learning system stack. System research for deep learning recommendation and AI at large is at a nascent stage. This talk will conclude with research directions for building and designing responsible AI systems - that is fair, efficient, and environmentally sustainable.
Speaker bio:
Carole-Jean Wu is a Technical Lead and Manager at Facebook AI Research - SysML. Her work is in the domain of computer system architecture with particular emphasis on energy- and memory-efficient systems. Her research has pivoted into designing systems for machine learning execution at-scale, such as for personalized recommender systems and mobile deployment. In general, she is interested in tackling system challenges to enable efficient, responsible AI execution. Carole-Jean chairs the MLPerf Recommendation Benchmark Advisory Board, co-chaired MLPerf Inference, and serves on the MLCommons Board as a director. Carole-Jean received her M.A. and Ph.D. from Princeton and B.Sc. from Cornell. She is the recipient of the NSF CAREER Award, Facebook AI Infrastructure Mentorship Award, the IEEE Young Engineer of the Year Award, the Science Foundation Arizona Bisgrove Early Career Scholarship, and the Intel PhD Fellowship, among a number of Best Paper awards.
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0:00 Starting Soon
4:46 Presentation
42:05 Discussion
The Stanford MLSys Seminar is hosted by Dan Fu, Karan Goel, Fiodar Kazhamiaka, and Piero Molino, Chris Ré, and Matei Zaharia.
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[@videowatching9576]

Awesome content, fascinating about opportunities for recommenders to improve thanks

[@nguyennguyenthithao6132]

Very good and funny videos bring a great sense of entertainment!

[@iversion4]

Are the slides available?

[@videowatching9576]

would be curious to hear more about examples of how recommenders might improve - put another way, in a scenario of 25% of papers delving into recommender models/systems up from single digit %, what kinds of advances might there be? How does that tie back into the DLRM open source as well to facilitate that or other things?

[@hershjoshi3549]

Why is so little of the community working on recommendation systems fields such as CV? While developing deep recommendation systems at Meta sounds like a dream job to me, being a salesperson was not why I joined this field, while CV and robotics often are.