Machine Learning: The High Interest Credit Card of Technical Debt
Linear Digressions
English - November 06, 2017 04:35 - 22 minutes - 10.2 MB - ★★★★★ - 350 ratingsTechnology data science machine learning linear digressions Homepage Download Apple Podcasts Google Podcasts Overcast Castro Pocket Casts RSS feed
Previous Episode: Improving Upon a First-Draft Data Science Analysis
Next Episode: The Kaggle Survey on Data Science
This week, we've got a fun paper by our friends at Google about the hidden costs of maintaining machine learning workflows. If you've worked in software before, you're probably familiar with the idea of technical debt, which are inefficiencies that crop up in the code when you're trying to go fast. You take shortcuts, hard-code variable values, skimp on the documentation, and generally write not-that-great code in order to get something done quickly, and then end up paying for it later on. This is technical debt, and it's particularly easy to accrue with machine learning workflows. That's the premise of this episode's paper.