Re - Release: Machine Learning Technical Debt

Re - Release: Machine Learning Technical Debt

This week, we've got a fun paper by our friends at Google about the hidden costs of maintaining machine learning workflows. If you've worked in software before, you're probably familiar with the idea of technical debt, which are inefficiencies that crop up in the code when you're trying to go fast. You take shortcuts, hard-code variable values, skimp on the documentation, and generally write not-that-great code in order to get something done quickly, and then end up paying for it later on. This is technical debt, and it's particularly easy to accrue with machine learning workflows. That's the premise of this episode's paper. https://ai.google/research/pubs/pub43146

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Avsnitt(321)

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3 Aug 25min

Distillation, or, How to Steal a Model

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27 Juli 23min

Invisible LLM Failures and AI Fluency with Chris Potts (Stanford)

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20 Juli 41min

Still summer break: back next week

Still summer break: back next week

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Summer break: back soon

Summer break: back soon

Summer break: back soon by Katie Malone

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