Machine Learning Engineering
topic on 2 shows · 4 statements across 2 episodes
the Y Combinator Startup Podcast 
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4 statements about Machine Learning Engineering, every show
Anthropic's Joseph: Very few engineers can debug ML from math to bytes
“I think one thing that's, like, surprisingly hard and there's very few people who can do is, like, kind of own that whole stack from, like, I understand how the ML is supposed to work and what the learning dynamics are, all the way down to, like, I know the by…”
Liu: Agency drives machine learning experiment volume; experience filters wasteful trials
“So, agency lets you sort of capture the volume of experiments, and, like, experience lets you figure out, like, oh, that other half, it's not worth doing.”
Liu: Define explicit conditions for revisiting negative machine learning experiment results
“Like what you should write down is like, here are the conditions. This is the inputs and the outputs we tried the experiment on. And then one thing that's really valuable is basically writing down under what conditions would I revisit these experiments?”