[QCon 2019] ML Panel

Hein Lu @Linked in, Brad Mitro @GoogleJeff Smith @ Facebook

For other QCon blog posts, see QCon live blog table of contents

Getting Started

  • People with other strong IT skills switched over
  • Can learn from books, coursera,, udacity, grad school
  • Look for specific applications
  • Domain is very large
  • Learn libraries, existing datasets
  • Understand where organization is at. Ex want to do ML vs specific problem
  • Focus on how will deliver business value


  • Many problems repeat so can get ideas from others
  • Important to have organizational alignment
  • Make sure to train on realistic data
  • Deep learning is very successful use case of ML
  • ”AI is the new electricity”
  • Limits of Moore’s law. Physical limitations with Quantum
  • Research on how to get algorithms to train theselve


  • PyTorch Hub

Learning resources


  • How learn without business case? How know what don’t know? Many educational resources start generally. Can skip some core concepts and learn later.
  • How pick good training data? Iterate on testing. Important to keep training with new data
  • Data heurisitcs? How much data? How many labels?
  • How make more agile? Use a pretrained model to start. Exist as a service or pull in via code
  • How know when good enough? Sometimes you have to just try. Or look to those who solved similar problems
  • Tech stack? Hardware acceleration. Iibraries
  • Fraud? Retrain data

My impressions

This was a good panel. Interesting responses. One panelist was missing, but it came out well

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