Welcome to AI! Welcome to machine learning! Does it matter if you don’t know the difference? Nope, because you’ll start applied projects in them the same way.
What way is that? Perhaps surprisingly, not any of these:
Look familiar? These tend to be favorite starting points, but they’re all traps. Many businesses fall for them and fail at machine learning, but not you. You’ll start right.
But first, why are these the favorites? It’s a story of comfort zones.
If you’re the studious type, your instinct might be to take a course or sign up for a degree. Watch out, though. The classic AI courses out there are probably wrong for your needs. I’d hate for you to wind up with the wrong degree!
If you’re a business leader, your instinct is to hire someone who sounds qualified. Great instinct! Except the person best qualified to start an applied AI project is not your garden variety AI PhD. It’s… you! Whoops. Hire yourself first and read on to find out what you’re supposed to do before you bring your champion nerd on board.
If you’re an AI researcher— recently hired to sprinkle some machine learning magic on top of the business, am I right?— you’ll want to start where you’re most comfortable. With the algorithm, naturally. You just spent 10 years of your life studying how to design new AI methods, so why would the leader want you to start elsewhere? Let’s pick an algorithm… mmmm,neural networksare all the rage. Maybe we can figure out how to make them even cooler? Let’s create a new approach! Now, what data can we shove into our new-ral network? (Here’s hoping we’ll wind up with something we can sell to the leader to justify the past six months spent inventing things.)
Or maybe you’re a data scientist. (Also a classic first hire, since today’s market thinks data scientists walk on water.) Perhaps you also have a PhD, but your Great Love isn’t methods. It’s data. Data data data! What data do we have? Let’s figure out what beautiful ingredients we can use!
Wait… use for what?
If you’re a data scientist or AI researcher and this sounds familiar, you just got handed a lemon by your leader. They let you down! Go on strike until they’ve done their part.
Leaders, figure out who’s calling the shots. If it’s you, then let’s designate you The Decision-Maker for this project. Otherwise, delegate the position to someone else and ask them to read the rest of this while you play outside in the sunshine.
Okay, Decision-Maker. It took a while to track you down, but here you are. You understand the business and you have plenty of imagination, so you’re qualified for this. Glad someone forwarded you this letter! Let’s get you oriented with how to set a machine learning (or AI) project up for success.
Imagine that this ML/AI system is already operating perfectly. Ask yourself what you would like it to produce when it does the next task. Don’t worry how it does it. Imagine that it works already and it is solving some need your business has. (That’s why you needed those qualifications. Someone fresh out of a PhD doesn’t understand your business yet, so they’re not qualified for this task.)
The problem with the approaches discussed previously is that the order of operations is all messed up. The right way to approach an applied project is to flip the algorithms-inputs-outputs order on its head, like so: think about outputs, then inputs, then algorithms!
A kitchen analogy comes in handy here. If you’re running a restaurant (as opposed to an appliance factory or food science lab), why would you think about buying — or, worse, inventing — a pizza oven before you’ve even considered whether adding pizza to your menu makes sense? That sounds like the rookie mistake of someone who doesn’t know what business they’re in. Instead, start with what your customers want and what food quality you’re willing to settle for.
Figuring out what success looks like can be nuanced.
