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

Important: We're skipping 26 October. Our next meetup is on 9 November, with a new paper!

On our next lunch meetup, we're going to discuss Machine Learning: The High Interest Credit Card of Technical Debt (https://research.google.com/pubs/pub43146.html), an interesting paper from a team of Google researchers demonstrating that use of machine learning has many costs as well as benefits. Beyond having one of the catchier titles in the history of computer science papers, the paper will keep your attention from beginning to end, an easy read with a great summary of real world experience maintaining large machine learning systems.

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.

The paper is available for free (click "PDF" at the link above).

Please consider joining us for lunch downtown and interesting conversation. See you there!