> Machine learning isn't about putting models into production. It's about machine learning models directly from data.
From a business perspective, the thing that's different about "machine learning" compared to other things you do with data is that it's possible to take the human out of the loop. That's a qualitative difference, as opposed to the quantitative difference of your business analysts giving better recommendations. We can quibble over terms, but as a broad stroke, things that are machine learning can do that and things that aren't machine learning cannot.
That qualitative difference is the main thrust of the quote you pulled, although it could be more explicit. Rather than the analyst building a model that tells him what shade of red is best for a button so that he can pass that information along to a design team, the button color is connected directly to the model.
The distinction, as you've restated it, still isn't useful:
'From a business perspective, the thing that's different about "machine learning" compared to other things you do with data is that it's possible to take the human out of the loop.'
There are many things you can do with data that take humans out of the loop, that don't involve machine learning. For example, software that automatically re-orders stock in a supermarket once stock (calculated based on starting stock less sales) goes below some level.
You could argue that this still has a human in the loop (to define a threshold) and that you're not removing the human from the loop until the thresholds themselves are automatically calculated.
But then you're just moving the job of the human from deciding the threshold, to deciding what % of the time it's acceptable to be out of stock of that item. Sure, you can automate that, too, but then the job of the human still exists: she's just deciding the objective function that stock-out percentage must satisfy, rather than deciding the stock-out percentage for each SKU directly using a jupyter notebook or Excel sheet.
I sincerely wish more people thought like this. Nothing is different about machine learning. It only performs better than OLS in a very specific subset of rich data, where improving prediction/action is important.
Totally fair, I guess what I was getting at (poorly) was that OLs has been around for a long time, lots of hype for ML now, but there are plenty of techniques here that have been readily available.
From a business perspective, the thing that's different about "machine learning" compared to other things you do with data is that it's possible to take the human out of the loop. That's a qualitative difference, as opposed to the quantitative difference of your business analysts giving better recommendations. We can quibble over terms, but as a broad stroke, things that are machine learning can do that and things that aren't machine learning cannot.
That qualitative difference is the main thrust of the quote you pulled, although it could be more explicit. Rather than the analyst building a model that tells him what shade of red is best for a button so that he can pass that information along to a design team, the button color is connected directly to the model.