MLOps Community Meetup #78 with Eugene Yan, an Applied Scientist at Amazon.
"Alibaba shared a paper about how they built embeddings for candidate retrieval. These embeddings use user behavior and they prepare the logs. From the user behavior, they can build a graph of the item. If a user browses a sequence of items they can use the relationship between each item and build a bidirectional graph." - Eugene Yan
//Abstract
How does system design for industrial recommendations and search look like? In this talk, Eugene Yan shares how its often split into:
- Latency-constrained online vs. less-demanding offline environments, and
- Fast but coarse candidate retrieval vs. slower but more precise ranking
We'll also see examples of system design from companies such as Alibaba, Facebook, JD, DoorDash, LinkedIn, and maybe do a quick walk-through on how to implement a candidate retrieval MVP.
//Bio
Eugene Yan designs, builds, and operates machine learning systems that serve customers at scale. He's currently an Applied Scientist at Amazon. Previously, he led the data science teams at Lazada (acquired by Alibaba) and uCare.ai. He writes & speaks about data science, data/ML systems, and career growth at eugeneyan.com and tweets at @eugeneyan.
// Relevant links
eugeneyan.com
applyingml.com
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