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System Design for Recommendations and Search

Posted Sep 15, 2021 | Views 512
# System Design
# Amazon
# Facebook
# DoorDash
# LinkedIn
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SPEAKER
Eugene Yan
Eugene Yan
Eugene Yan
Applied Scientist @ Amazon

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.

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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.

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SUMMARY

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.

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