Hidden Technical Debt in Machine Learning Systems
In this reference
A study of the maintenance costs that emerge around machine learning systems, including data dependencies, feedback loops and tightly coupled components.
Cited in my work
Cite the original work
D. Sculley & Gary Holt & Daniel Golovin & Eugene Davydov & Todd Phillips & Dietmar Ebner & Vinay Chaudhary & Michael Young & Jean-François Crespo & Dan Dennison (2015). Hidden Technical Debt in Machine Learning Systems [Research paper]. NeurIPS. https://proceedings.neurips.cc/paper/2015/hash/86df7dcfd896fcaf2674f757a2463eba-Abstract.html
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@misc{hidden_technical_debt,
author = {D. Sculley and Gary Holt and Daniel Golovin and Eugene Davydov and Todd Phillips and Dietmar Ebner and Vinay Chaudhary and Michael Young and Jean-François Crespo and Dan Dennison},
title = {{Hidden Technical Debt in Machine Learning Systems}},
howpublished = {NeurIPS},
url = {https://proceedings.neurips.cc/paper/2015/hash/86df7dcfd896fcaf2674f757a2463eba-Abstract.html},
year = {2015}
}
