Data-Driven Leadership and Careers

Getting started with AI? Start here!

Everything you need to know to dive into your project

Many teams try to start an applied AI project by diving into algorithms and data before figuring out desired outputs and objectives. Unfortunately, that’s like raising a puppy in a New York City apartment for a few years, then being surprised that it can’t herd sheep for you.

You can’t expect to get anything useful by asking wizards to sprinkle machine learning magic on your business without some effort from you first.

Instead, the first step is for the owner — that’s you! — to form a clear vision of what you want from your dog (or ML/AI system) and how you’ll know you’ve trained it successfully.

My previous article discussed the why, now it’s time to dive into how to do this first step for ML/AI, with all its gory little sub-steps.

This reference guide is densely-packed and long, so feel free to stick to large fonts and headings for a two-minute crash course or head straight to the summary checklist version. Here’s the table of contents:

• Figure out who’s in charge