Episode 27 of the Stanford MLSys Seminar Series!
Deep Learning Based Recommender Systems in Production
Speaker: Even Oldridge
Recommender Systems are one of the most complex ML applications to deploy into production. The data is sparse, massive, and constantly increasing, and the models deployed create a feedback loop that requires careful monitoring. What's more, the hardware and software that led to the revolution of deep learning was built during the era of computer vision. Differences in architecture and data between vision and recommenders initially made the HW/SW stack a poor fit for deep learning based recommender systems. In this talk we'll explore what makes recommenders different from a data, architecture, and system perspective, and talk about changes in GPU hardware within the last generation that make it much better suited to the recommendation problem. By focusing on these differences we've also identified improvements on the software side that take advantage of optimizations only possible in the recommendation domain. A new era of faster ETL, Training and Inference is coming to the RecSys space and this talk will walk through some of the patterns of optimization that guide the tools we're building to make recommenders both faster to use and easier to deploy on GPUs.
Even Oldridge is a Sr. Manager at NVIDIA leading the effort to develop the open source libraries of Merlin which provide fast, easy to use and deploy, scalable recommender systems on the GPU. He has a PhD in Computer Vision and a Masters in Programmable Hardware from the University of British Columbia. He’s worked in the recommendation space for the past decade and has developed systems for recommending dates and houses, among other things. He’s an industry co-chair for ACM RecSys Conference 2021, and he’ll talk your ear off about embeddings and deep learning based recommenders if you let him.
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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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