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Making AI Happen Without Getting Fired

From H2O.ai

I watched Mike Gualtieri’s keynote presentation from H2O World San Francisco (2019) and found it to be very insightful from a non-technical MBA type of way. The gist of the presentation is to really look at all the business connections to doing data science. It’s not just about the problem at hand but rather setting yourself up for success, and as he puts it, not getting fired!

My notes from the video are below (emphasis mine):

  • Set the proper expectations
  • There is a difference between Pure AI and Pragmatic AI
  • Pure AI is like what you see in movies (i.e. ExMachina)
  • Pragmatic AI is machine learning. Highly specialized in one thing but does it really well
  • Chose more than one use case
  • The use case you choose could fail. Choose many different kinds
  • Drop the ones that don’t work and optimize the ones that do
  • Ask for comprehensive data access
  • Data will be in silos
  • Get faster with AutoML
  • Data Scientists aren’t expensive, they need better tools to be more efficient
  • Three segments of ML tools
    • Multimodel (drag and drop like RapidMiner/KNIME)
    • Notebook-based (like Jupyter Notebook)
    • Automation-focused (like Driverless AI)
  • Use them to augment your work, go faster
  • Warning: Data-savvy users can use these tools to build ML. Can be dangerous but they can vet use cases
  • Know when to quit
  • Sometimes the use case won’t work. There is no signal in the data and you must quit
  • Stop wasting time
  • Keep production models fresh
  • When code is written, it’s written the same way and runs the same forever
  • ML Models decay, so you need to figure out how to do it at scale
  • Model staging, A/B testing, Monitoring
  • Model deployment via collaboration with DevOps
  • Get Business and IT engaged early
  • They have meetings with business and IT, get ducks in a row
  • Ask yourself, how is it going to be deployed and how it will impact business process
  • Ignore the model to protect the jewels
  • You don’t have to do what the model tells you to do (i.e False Positives, etc)
  • Knowledge Engineering: AI and Humans working together
  • Explainability is important
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