Over the last 8 weeks I have looked to cover key learnings from my 21 years building Machine Learning models in Financial Services. This is such a large topic and as such this is by no means comprehensive but the linked 8 microblogs cover the most important topics as you look to build your models.
If you’ve missed them there are quick links below:
- The best thing you can do to have AI success (or why a documented and agreed AI review and sign-off process is essential)
- Avoid finding AI down the back of the sofa (or why inventory of AI is important)
- The need for bias understanding (or why it matters what’s in the black box)
- But is it working? (or why deploying your model is just the start)
- What can go wrong... and what will you do about it? (or why everything comes back to great risk management)
- Help out your future self (or why I love building flat-pack furniture)
- Opening up the watch (more on understanding that black box)
- Occam’s Razor (or how to balance complexity with power)?
Next week I will turn my thoughts to AI governance starting with how to avoid the Sharks vs. the Jets.
