Hidden Technical Debt in Machine Learning Systems
Machine learning offers a fantastically powerful toolkit for building useful com-plex prediction systems quickly. This paper argues it is dangerous to think of these quick wins as coming for free. Using the software engineering framework of technical debt, we find it is common to incur massive ongoing maintenance costs in real-world ML systems.
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