Machine Learning: The High-Interest Credit Card of Technical Debt

https://static.googleusercontent.com/media/research.google.com/en/us/pubs/archive/43146.pdf

Machine learning offers a fantastically powerful toolkit for building complex systems quickly. This paper argues that it is dangerous to thinkof 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 atthe system levelwhen 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