Comment by sidlls

I think the criticism is a bit over the top to be honest but not by much.

ML seems to be something an organization reaches for most often when: a) it doesn't understand the data it has and doesn't have individuals who have the competencies to hire analysts to help them; b) when they want to tell a story to an audience of engineers (e.g. as to attract "talent" or signal about how bleeding edge the tech is in the company).

A far less frequent case is when individuals with actual expertise have identified a real need for the use of the statistical methods and infrastructure used in ML applications. In this sense it's a scam--but one that technology organizations use against themselves.

The real money in ML, just like with most fads, is in selling the tools, not doing the work. Hence you see the rise of all these "ML ops" platform type businesses or business units (see: Sagemaker, Databricks, and various others).