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.
In this episode
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.
Demetrios is one of the main organizers of the MLOps community and currently resides in a small town outside Frankfurt, Germany. He is an avid traveller who taught English as a second language to see the world and learn about new cultures. Demetrios fell into the Machine Learning Operations world, and since, has interviewed the leading names around MLOps, Data Science, and ML. Since diving into the nitty-gritty of Machine Learning Operations he felt a strong calling to explore the ethical issues surrounding ML. When he is not conducting interviews you can find him making stone stacking with his daughter in the woods or playing the ukulele by the campfire.