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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.

Entanglement. Machine learning systems mix signals together, entangling them and making iso-lation of improvements impossible. For instance, consider a system that uses features x1,...x n in a model. If we change the input distribution of values in x1, the importance, weights, or use of the remaining n − 1features may all change.

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