As a classic example, how about music or movie recommendations? You have a giant set of attributes about songs and a binary classification (thumbs up/thumbs down) from millions of listeners. You can use this this to train a random forest or neural net (or other algorithms) to make song recommendations to someone based on that person's pattern of thumbs up/down. I'd guess most recommendation systems do something along these lines, though I don't really know.
I'm not sure how I'd do this with SQL. That certainly doesn't mean you couldn't take a SQL approach to the problem of "how do I make recommendations based on this training set and this particular individual's attributes" and do something creative and useful. But I do think you can, in this situation, get something out of ML that would be difficult (ok, I'll say impossible) to get with a pure SQL approach.
To be clear, I love SQL. I still get a little flash of anger when I remember my arguments with "architects" who announced to a room full of non-technical people that I was pretty much a dinosaur unwilling to move away from an obsolete technology when they advocated no-sql approaches to data that was deeply relational. And I'm sure that there are plenty of people who think "need answer from data, must use ML" and end up somehow trying to train a neural net to perform a WHERE clause. Having seen the hype cycle several times, I am absolutely certain this has occurred in boardrooms/open office cubicle farms all across the you ess aye and beyond.
But keep in mind, even though reports of SQL's death were remarkably exaggerated, "nosql" approaches often do make the most sense. Plenty of good ideas and technologies came from nosql, and many of the people who created them and advocated them continued to use (and argue for) SQL and relational databases depending on the problem. A graph database is a vastly superior approach to some types of network problems than standard SQL, and not helpful for others. When the hype cycle around ML dies down (and some people start writing articles that ML is "dead" or whatever), ML will continue to be used effectively in all kinds of places, as it is now.
It was clear that you were only saying that it wasn't ML.
I looked up a bit on elasticsearch's text classification, and it's interesting. There's a overview of "traditional" ML (similar to the song review classification I mentioned) and how elastic search differs.
"The MLT query is a very important query for text mining. How does it work? It can process arbitrary text, extract the top n keywords relative to the actual "model" and run a boolean match query with those keywords. This query is often used to gather similar documents."
Yeah, looks like aggregation and scoring. It'd be interesting to see if/where this outperformed various ML algorithms (accuracy as well as performance).
I'm not sure how I'd do this with SQL. That certainly doesn't mean you couldn't take a SQL approach to the problem of "how do I make recommendations based on this training set and this particular individual's attributes" and do something creative and useful. But I do think you can, in this situation, get something out of ML that would be difficult (ok, I'll say impossible) to get with a pure SQL approach.
To be clear, I love SQL. I still get a little flash of anger when I remember my arguments with "architects" who announced to a room full of non-technical people that I was pretty much a dinosaur unwilling to move away from an obsolete technology when they advocated no-sql approaches to data that was deeply relational. And I'm sure that there are plenty of people who think "need answer from data, must use ML" and end up somehow trying to train a neural net to perform a WHERE clause. Having seen the hype cycle several times, I am absolutely certain this has occurred in boardrooms/open office cubicle farms all across the you ess aye and beyond.
But keep in mind, even though reports of SQL's death were remarkably exaggerated, "nosql" approaches often do make the most sense. Plenty of good ideas and technologies came from nosql, and many of the people who created them and advocated them continued to use (and argue for) SQL and relational databases depending on the problem. A graph database is a vastly superior approach to some types of network problems than standard SQL, and not helpful for others. When the hype cycle around ML dies down (and some people start writing articles that ML is "dead" or whatever), ML will continue to be used effectively in all kinds of places, as it is now.