Transcription of Hidden Technical Debt in Machine Learning Systems
{{id}} {{{paragraph}}}
Example: barber
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. These include boundary erosion, entanglement, Hidden feedback loops, undeclared consumers, data dependencies, configurationissues, changes in the external world, and a variety of system -level IntroductionAs the Machine Learning (ML) community continues to accumulate years of experience with livesystems, a wide-spread and uncomfortable trend has emerged: developing and deploying ML sys-tems is relatively fast and cheap, but maintaining them overtime is difficult and dichotomy can be understood through the lens oftechnical debt , a metaphor introduced byWard Cunningham in 1992 to help reason about the long term costs
Google, Inc. Abstract Machine learning offers a fantastically powerful toolkit for building useful com-plex prediction systems quickly. This paper argues it is dangerous to think of these quick …
Loading..
Tags:
Information
Domain:
Source:
Link to this page:
Please notify us if you found a problem with this document: