File:NIPS-5656-hidden-technical-debt-in-machine-learning-systems.pdf

From Federation of Earth Science Information Partners

NIPS-5656-hidden-technical-debt-in-machine-learning-systems.pdf(file size: 162 KB, MIME type: application/pdf)

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."

File history

Click on a date/time to view the file as it appeared at that time.

Date/TimeDimensionsUserComment
current21:21, October 7, 2018 (162 KB)Anne Wilson (talk | contribs)http://papers.nips.cc/paper/5656-hidden-technical-debt-in-machine-learning-systems.pdf "Machine learning offers a fantastically powerful toolkit for building useful complex prediction systems quickly. This paper argues it is dangerous to think of thes...

The following page uses this file: