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Machine Learning: The High-Interest Credit Card of Technical Debt
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.
1807  dev  wrk 
july 2018 by rdslw
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