MLOps Coffee Sessions #130 {Podcast BTS} with Andrew Yates, Adversarial MLOps on Other People's Money: Lessons Learned from Ad Tech, co-hosted by Abi Aryan.
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// Abstract
Design ML to be components in a larger system with stable interfaces is not traceable to monitor the entire stack as a black box. You need intermediate ground-truth signals. Ads are designed in this way.
You can go from profitable to non-profitable real quick with ads. This will determine whether your company is around a year or two. You play with money, and sometimes you play a lot of it, so make sure that it's correct.
// Bio
Andrew Yates formerly led ads ranking, auction, and marketplace engineering and research teams at Facebook and Pinterest. He specializes in designing billion-dollar content marketplaces that maximize long-term revenue while protecting both seller and user experiences. Andrew has published over a dozen patents in online advertising optimization.
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Connect with Andrew on LinkedIn: https://www.linkedin.com/in/andrew-yates-0217a985/
Timestamps:
[00:00] Introduction to Andrew Yates and takeaways
[03:26] Want more like this episode?
[03:53] Andrew's Background
[05:29] How did he get into adtech?
[09:40] Evolution of adtech
[12:30] Challenges they face
[14:04] The structures of teams in bigger tech companies
[21:12] Search and discovery teams in bigger tech companies
[23:10] Strategy around technical debt
[28:40] Promoted.ai for big marketplaces
[30:18] How Andrew fits into teams
[33:53] Engineering challenges when working in a small team
[37:47] How much white-gloving they do amid complexity
[39:32] Allowing companies to plug in their models into Promoted
[41:58] Drawbacks of doing real-time streaming
[48:06] Wrap up
