Lessons from Studying FAANG ML Systems // Ernest Chan // MLOps Coffee Sessions #84

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MLOps Coffee Sessions #84 with Ernest Chan, Lessons from Studying FAANG ML Systems.
// Abstract
Large tech companies invest in ML platforms to accelerate their ML efforts. Become better prepared to solve your own MLOps problems by learning from their technology and design decisions.
Tune in to learn about ML platform components, capabilities, and design considerations.
// Bio
Ernest is a Data Scientist at Duo Security. As part of the core team that built Duo's first ML-powered product, Duo Trust Monitor, he faced many (frustrating) MLOps problems first-hand. That led him to advocate for an ML infrastructure team to make it easier to deliver ML products at Duo. Prior to Duo, Ernest worked at an EdTech company, building data science products for higher-ed. Ernest is passionate about MLOps and using ML for social good.
// Related Links
Lessons on ML Platforms — from Netflix, DoorDash, Spotify, and more: https://ernestklchan.medium.com/lessons-on-ml-platforms-from-netflix-doordash-spotify-and-more-f455400115c7
Paper Highlights-Challenges in Deploying Machine Learning: a Survey of Case Studies https://towardsdatascience.com/paper-highlights-challenges-in-deploying-machine-learning-a-survey-of-case-studies-cafe61cfd04c
Choose boring technologies Slideshare by Dan McKinley: https://www.slideshare.net/danmckinley/choose-boring-technology
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Connect with Ernest on LinkedIn: https://www.linkedin.com/in/ernest-chan-68245773/
Timestamps:
[00:00] Introduction to Ernest Chan
[01:07] Takeaways
[02:58] Ernest's Lessons on ML Platforms — from Netflix, DoorDash, Spotify, and more blog post
[05:55] Five components of an ML Platform
[10:09] Limitations highlighted in the blog post
[14:41] Level of maturity or completion observed in company efforts
[16:17] Platform/Architecture admired the most
[17:46] Advice to big tech companies
[22:03] Process of needing an infrastructure and aiming towards having a platform
[24:23] Paper Highlights-Challenges in Deploying Machine Learning: a Survey of Case Studies blog post
[26:24] Takeaways from Paper Highlights-Challenges in Deploying Machine Learning
[30:33] Prioritization
[33:04] Delta Lake
[35:27] Model rollouts and shadow mode
[39:23] Are you an ML Engineer or a Data Scientist?
[40:15] Simple route platform vs flexible platform trade-offs
[41:08] Opinionated and simple vs less opinionated and flexible
[43:22] Choose boring technologies Slideshare by Dan McKinley
[44:36] Wrap up