[ machinelearning engineering production

39 Lessons on Building ML Systems, Scaling, Execution, and More

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2024-11-23 05:00:02

[ machinelearning engineering production leadership ] · 10 min read

Industry ML conferences are intense. There’s so much information, learning, and context switching between talks and posters and hallway conversations that leaves you exhausted each day. Thus, whenever there’s a break, taking a few minutes to reflect and take notes helps to solidify the learning. Here are my notes from ML conferences in 2024.

(I also had the opportunity to share my work at a few of these conferences. Here are the slides for my talks at the Netflix PRS Workshop and the AI Engineer World’s Fair. Unfortunately, my oral presentation at the Amazon ML Conference is internal only.)

1. The real world is messy. To build systems that work, we need to define reward functions (that define labels), operationalize the world as data, find levers that make a difference, and measure what matters. Beware of those who tell you ML is a walk in the park.