A trigger list for technical debt in machine learning

Technical debt doesn’t appear in neat, labelled units. It builds up slowly, insidiously—especially in machine learning systems. It starts as a feeling. Features become hard to implement because of complicated interactions and dependencies. Re-training and deploying your classifiers becomes increasingly tedious. And when you do become aware of technical debt, you’re not always in the right state of mind to start writing a list or to plan how to fix the thing.

So, to notice technical debt early, I look for it as part of a periodic review, and I use a trigger list for this.1 It’s based off of this paper (Hidden Technical Debt in Machine Learning Systems) and my personal experience. I’m not going to describe what all of these triggers mean. They don’t even have to mean the same thing to you as they do to me. But, this list helps me to recognize and itemize the technical debt in my machine learning systems.

  • The first thing you wrote
  • Documentation
  • Dead code
  • Unit tests
  • Software dependencies
  • API mess
  • Data that breaks abstraction boundaries
  • Live monitoring of performance
  • Correction cascades
  • Who is using each module’s output
  • Data dependencies
  • Legacy features2
  • Bundled features2
  • Epsilon features2
  • Correlated features2
  • Glue code
  • Pipeline jungles
  • Dead flags
  • Plain old data structures
  • Multiple languages
  • Left-over prototypes
  • Configuration debt
  • Versioning
  • Data for testing
  • Reproducibility

1. Every good productivity or planning system includes something like a weekly review. These reviews let me be sure of my direction as I navigate each week. They also clear my head so that I am free to just focus, relax, or do nothing, without any worry that there is anything else I should be doing. I’ve found this trigger list to be useful during these weekly reviews.

2. “Features” here means features, or variables, or covariates, in a model.

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