Hidden Technical Debt In Machine Learning Systems
Olivia Luz
Sculley et al 2015.
Hidden technical debt in machine learning systems felipe 23 mar 2020 29 mar 2020 paper summary machine learning engineering technical debt scikit learn pipelines. Long term maintenance of these ml systems is getting more involved than traditional systems due to the additional challenges of data and other specific ml issues 1. Using the software engineering frameworkof technical debt we find it is common to incur massive ongoing maintenancecosts in real world ml systems. Artificial intelligence ai and machine learning ml systems have a special ability to increase technical debt.
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. Hidden technical debt in machine learning systems. This paper argues it is dangerous to think of these quick wins as coming for free.
Request pdf hidden technical debt in machine learning systems machine learning offers a fantastically powerful toolkit for building useful com plex prediction systems quickly. Download here or click on the pdf icon want to learn more about this paper. In this article we have covered a few hidden technical debt of the ml system which is summarized in the below table with some possible mitigation strategies. This paper argues it is dangerous to think ofthese quick wins as coming for free.
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Hidden technical debt in machine learning systems d. Here s an abstract from the paper.
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