ML Anthology

Hidden Technical Debt in Machine Learning Systems

D. Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-François Crespo, Dan Dennison
NeurIPS 2015 pp. 2503-2511
/neurips/2015/sculley2015neurips-hidden/

Abstract

Machine learning offers a fantastically powerful toolkit for building useful complexprediction systems quickly. This paper argues it is dangerous to think ofthese quick wins as coming for free. Using the software engineering frameworkof technical 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 anti-patterns.

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Text

Sculley et al. "Hidden Technical Debt in Machine Learning Systems." Neural Information Processing Systems, 2015.

Markdown

[Sculley et al. "Hidden Technical Debt in Machine Learning Systems." Neural Information Processing Systems, 2015.](https://mlanthology.org/neurips/2015/sculley2015neurips-hidden/)

BibTeX

@inproceedings{sculley2015neurips-hidden,
  title     = {{Hidden Technical Debt in Machine Learning Systems}},
  author    = {Sculley, D. and Holt, Gary and Golovin, Daniel and Davydov, Eugene and Phillips, Todd and Ebner, Dietmar and Chaudhary, Vinay and Young, Michael and Crespo, Jean-François and Dennison, Dan},
  booktitle = {Neural Information Processing Systems},
  year      = {2015},
  pages     = {2503-2511},
  url       = {https://mlanthology.org/neurips/2015/sculley2015neurips-hidden/}
}