[nostrademons]

It can't, though. ML works best when you have enough data to build models that are robust and resilient to noise, and enough customers (or users) that these models will move the needle.

The vast majority of startups and small businesses - those whose customer base measures in the dozens to hundreds - should be going out, engaging their customers person-to-person, and looking for qualitative data, because that's what'll move the needle on their sales. There's no point in understanding "your customer base" as a unit until it's big enough that it behaves, statistically, as a unit; instead, you should be focusing on "your customers", individually. Once you get into the thousands of customers you can start applying some basic learning models, and once you get into the millions machine-learning becomes as fundamental as pricing.

But you gotta get there first, and many businesses haven't. And even if they have, userbase-wise, they need to build the infrastructure (through web & mobile devs, backend engineers, data scientists, etc.) to log, store, and clean all that data before they can apply machine-learning to it.

[mindcrime]

But you gotta get there first, and many businesses haven't.

Agreed. But many have as well. So I'll still argue that there are more potential positions for people doing "applied ML" than there are for quants. I'm open to being proven wrong though.

And even if they have, userbase-wise, they need to build the infrastructure (through web & mobile devs, backend engineers, data scientists, etc.) to log, store, and clean all that data before they can apply machine-learning to it.

We're working on a MLaaS offering to help reduce the need for a lot of that stuff. And there are some offerings in that space already.