ML@SCALE 1:1

Answer these questions as candidly as you can. Long-form responses are welcome.

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The 60-second pitch ^*

Who are you, where do you work, and what ML system are you actually responsible for?

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The stack, end to end ^*

From data ingestion to serving. What's custom, what's off-the-shelf, and what would you rebuild if you started today?

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Your most expensive mistake in production ^*

Expensive in time, compute, or credibility. What happened and what did it teach you?

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The thing nobody says out loud ^*

What does ML at scale actually look like versus what gets published on the engineering blog?

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Where research meets reality ^*

Your company produces some of the best ML research in the world. How much of it actually makes it into production, and why?

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What you'd tell someone joining a big tech ML ^*

What advice would you give someone starting as an ML engineer or applied scientist at a big tech company?

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