Re - Release: Machine Learning Technical Debt
Published May 12, 2019
|
22 min
    Download
    Add to queue
    Copy URL
    Show notes
    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. https://ai.google/research/pubs/pub43146
      15
      15
        0:00:00 / 0:00:00