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Hidden Technical Debt in Machine Learning Systems

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 . We explore several ML-specific risk factors toaccount for in system design. These include boundary erosion, entanglement, Hidden feedback loops, undeclared consumers, data dependencies, configurationissues, changes in the external world, and a variety of system-level IntroductionAs the Machine Learning (ML) community continues to accumulate years of experience with livesystems, a wide-spread and uncomfortable trend has emerged: developing and deploying ML sys-tems is relatively fast and cheap, but maint

Hidden Technical Debt in Machine Learning Systems D. Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips {dsculley,gholt,dgg,edavydov,toddphillips}@google.com

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