Machine Learning: The High Interest Credit Card of Technical Debt
Last updated: 2022-11-27 Sunday
Machine Learning: The High Interest Credit Card of Technical Debt
A presentation for NIPS2014 by D. Sculley Gary Holt Daniel Golovin Eugene Davydov Todd Phillips Dietmar Ebner Vinay Chaudhary Michael Young
SE4ML: Software Engineering for Machine Learning (NIPS 2014 Workshop)
what is it about
Machine learning offers a fantastically powerful toolkit for building complex systems quickly. This paper argues that it is dangerous to think of these quick wins as coming for free. Using the framework of technical debt, we note that it is remarkably easy to incur massive ongoing maintenance costs at the system level when applying machine learning. The goal of this paper is highlight several machine learning specific risk factors and design patterns to be avoided or refactored where possible. These include boundary erosion, entanglement, hidden feedback loops, undeclared consumers, data dependencies, changes in the external world, and a variety of system-level anti-patterns.
what do I think about it
Gives a great overview of where ML development can go wrong.