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

Getting Started in Data Science Part 2

I’m finally getting around to writing Part 2 of Getting Started in Data Science. The first part can be found here. I made suggestions for university students interested in the field of Data Science. I even made a video about it too. 

Pick Two, Master One

Pick two computer languages and become proficient in one and a master at the other one. Or, pick a platform like H2O-Flow or RapidMiner and a language. Become a master at one but proficient in the other. This way you can set yourself apart from other students or applicants. 

Continue reading “Getting Started in Data Science Part 2”

Got MBA? Buy that the Right Business!

Brian tweeted an interesting link the other day about a new trend that HBR picked up on. It was about newly minted MBA graduates buying existing businesses instead of going into right into the consulting world.

At the Harvard Business School, for example, the number of MBAs who decide to look for a business to acquire right after graduation has gone from less than a handful a decade ago to more than a dozen, and in an occasional year, twice that amount.

I’m surprised that this number isn’t higher but it’s probably because of the stress that these students experience in the search for that special business.

One of the most common concerns that we hear as we advise our students at HBS is that searching for a business to buy — a full-time endeavor — is too risky.”

It appears that financial risk has less weight than the finding the right business to buy. This is interesting but makes complete sense to me.

If you want to be an entrepreneur and be successful, you need to either fill a need or solve a problem in an industry that you are passionate about.

This is why the search for that right business can take a long time and requires lots of reflection. Just because a business might be cheap to buy, it might not have long term potential. This is key. You want to find that business that has the potential to disrupt the status quo.

Now go forth and find it.