Machine Learning Cohorts: A Synthesis

This largely misses the mark. Kaggle is a machine learning competition platform for a small set of exceptional machine learning talent, and a lot of students or hangers-on.

"Machine learning projects – if ML is being attempted at all – are in early stages, using traditional methods that are best-suited for high-RAM CPU rather than GPU SKUs (ex: scikit-learn and clustering approaches)."

The idea that the machine learning being done there is in "early stages" is laughable, given the prize pools and sheer competitiveness usually move well past existing SOTA--usually moving forward benchmarks on Google's image classification and labeling (!) benchmarks and other areas where enormous teams can't match the top few.

Part of what you're seeing is the mass of survey participants, who show up to fork notebooks and fake ML skills, are the people who haven't exactly established themselves in the field (https://commons.m.wikimedia.org/wiki/File:Survivorship-bias....), while the top-end is too small a group to fully characterize with low-powered clustering.

Even five years ago, most real-world MLEs knew how to use AWS, were deploying actual machine learning models (granted, more primitive than today's methods), and I'm wouldn't be so glib about calling anything "early stage" even then given the raw business value it provides.