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'''Hidden Technical Debt in Machine Learning Systems'''

D. Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Tod Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-Francois Crespo, Dan Dennison, Advances in Neural Information Processing Systems, 2015, http://papers.nips.cc/paper/5656-hidden-technical-debt-in-machine-learning-systems.

"Machine learning offers a fantastically powerful toolkit for building useful complex
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. We explore several ML-specific risk factors to
account for in system design. These include boundary erosion, entanglement,
hidden feedback loops, undeclared consumers, data dependencies, configuration
issues, changes in the external world, and a variety of system-level anti-patterns."