Data-Driven Leadership and Careers

Why businesses fail at machine learning

I’d like to let you in on a secret: when people say ‘machine learning’ it sounds like there’s only one discipline here. There are two, and if businesses don’t understand the difference, they can experience a world of trouble.

A tale of two machine learnings

Imagine hiring a chef to build you an oven or an electrical engineer to bake bread for you. When it comes to machine learning, that’s the kind of mistake I see businesses making over and over.

These are different businesses! Unfortunately, too many machine learning projects fail because the team doesn’t know whether they’re supposed to build the oven, the recipe, or the bread.

If you’re opening a bakery, it’s a great idea to hire an experienced baker well-versed in the nuances of making delicious bread and pastry. You’d also want an oven. While it’s a critical tool, I bet you wouldn’t charge your top pastry chef with the task of knowing how to build that oven; so why is your company focused on the equivalent for machine learning?

Are you in the business of making bread? Or making ovens?

Machine learning research