Hidden Technical Debt in Machine Learning Systems - NIPS

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Hidden Technical debt in Machine Learning SystemsD. Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Ebner, Vinay Chaudhary, Michael Young, Jean-Franc ois Crespo, Dan Learning offers a fantastically powerful toolkit for building useful com-plex prediction Systems quickly. This paper argues it is dangerous to think ofthese quick wins as coming for free. Using the software engineering frameworkoftechnical debt , we find it is common to incur massive ongoing maintenancecosts in real-world ML Systems .

consistency of the information inputs and outputs from an given component [8]. Unfortunately, it is difficult to enforce strict abstraction boundaries for machine learning systems by prescribing specific intended behavior. Indeed, ML is required in exactly those cases when the

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