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Paper from 2014 titled: Machine Learning: The High Interest Credit Card of Technical Debt Authored by a list of Googlers: D. Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young Abstract: Machine learning offers a fantastically powerful toolkit for building complex systems quickly. This paper argues that it is dangerous to think of these quick wins as coming for free. Using the framework of technical debt, we note that it is remarkably easy to incur massive ongoing maintenance costs at the system level when applying machine learning. The goal of this paper is highlight several machine learning specific risk factors and design patterns to be avoided or refactored where possible. These include boundary erosion, entanglement, hidden feedback loops, undeclared consumers, data dependencies, changes in the external world, and a variety of system-level anti-patterns.

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Paper from 2014 titled: Machine Learning: The High Interest Credit Card of Technical Debt Authored by a list of Googlers: D. Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young Abstract: Machine learning offers a fantastically powerful toolkit for building complex systems quickly. This paper argues that it is dangerous to think of these quick wins as coming for free. Using the framework of technical debt, we note that it is remarkably easy to incur massive ongoing maintenance costs at the system level when applying machine learning. The goal of this paper is highlight several machine learning specific risk factors and design patterns to be avoided or refactored where possible. These include boundary erosion, entanglement, hidden feedback loops, undeclared consumers, data dependencies, changes in the external world, and a variety of system-level anti-patterns.
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Jed Brown (jedbrown@hachyderm.io)'s status on Monday, 13-Feb-2023 20:56:05 JST Jed Brown

All the hype about souped up developer productivity using LLMs for coding reminds me of the original title of this 2014 paper, before it was milquetoasted in 2015 acceptance.
LLMs can help you rapidly acquire semi-plagiarized fragments of well-traveled code instead of using a quality library with vision of the problem domain. Might be great for KPIs, but this debt will come back to bite you, unless you're already gone. Will be painful for orgs to adapt.
https://research.google/pubs/pub43146/

In conversation Monday, 13-Feb-2023 20:56:05 JST from hachyderm.io permalink