System Design for Recommendations and Search

this is an online event, please register at the following link: https://www.aicamp.ai/event/eventdetails/W2021071312

Agenda
12 pm -- 12:05 pm member online
12:05 pm -- 12:50 pm Talk + QA
12:50 pm -- 1 pm Closing

Topic: System Design for Recommendations and Search

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.

Speaker: Eugene Yan (Amazon)

Speaker 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.

Reference blog by Eugene related to this talk
https://eugeneyan.com/writing/system-design-for-discovery/