Interesting reads from my random walk over the internet

1. Enterprise Software Monetization is Fat-Tailed
2. Microsoft is allowing employees to go remote permanently
3. Notes from Work Rules on hiring
4. Limitations of Graph Neural Networks
5. Running Python on .NET 5
6. Why are tech companies making custom typefaces
7. Anatomy of a probabilistic programming framework
8. Will the M1 macs run the PyData stack
9. Electric bicycles that double up as cars
10. Avoiding technical debt with ML pipelines

This was inspired by my participation in the TAO Data Science Panel.

I'm starting to see a bifurcation in research vs business data science.

• Research vs Business Data Science

How this translates to training needs and hiring

• Researchers think mechanistically about the world
• Learn how to learn fast
• Operate outside your pay grade
• Data scientists should know the data generating process behind their data
• Data scientists should learn how to write good code
• Avoiding technical debt with ML pipelines

And notes for managing data scientists:

• How to motivate your problem solvers