Deep learning job postings have collapsed in the past six months

That and he makes these tweets about threats and insults from "people using Pytorch" and the TensorFlow/Keras vs Pytorch "debate" without taking a screenshot or actually showing any kind of proof.

He seems pretty oblivious to the fact that simply not mentioning them would make the problem go away as no one beside him seems to actually care.

I've seen similar patterns with clients and companies I've worked at as well. My experience was less that ML wasn't useful, it's just that no organization I worked with could really break down the silos in order for it to work. Especially in ML, the entire process from data collection to the final product and feedback loop needs to be integrated. This is _really_ difficult for most companies.

Many data scientists I knew were either sitting on their hands waiting for data or working on problems that the downstream teams had no intention of implementing (even if they were improvements). I still really believe that ML (be it fancy deep learning or just evidence driven rules-based models) will effectively be table stakes for most industries in the upcoming decade. However, it'll take more leadership than just hiring a bunch of smart folks out of a PhD program.

Ironically, I worked on a product that had a classic use case for machine learning during this time period and still had great difficulty getting results.

It was difficult to attract top ML talent no matter how much we offered. Everyone wanted to work for one of the big, recognizable names in the industry for the resume name recognition and a chance to pivot their way into a top role at a leading company later.

Meanwhile, we were flooded with applicants who exaggerated their ML knowledge and experience to an extreme, hoping to land high paying ML jobs through hiring managers who couldn’t understand what they were looking for. It was easy to spot most of these candidates after going through some ML courses online and creating a very basic interview problem, but I could see many of these candidates successfully getting ML jobs at companies that didn’t know any better. Maybe they were going to fake it until they made it, or maybe they were counting on ML job performance being notoriously difficult to quantify on big data sets.

Dealing with 3rd party vendors and consulting shops wasn’t much better. A lot of the bigger shops were too busy with never ending lucrative contracts to take on new work. A lot of the smaller shops were too new to be able to show us much of a track record. Their proposals often boiled down to just implementing some famous open source solution on our product and letting us handle the training. Thanks, but we can do that ourselves.

I get the impression that it is (or was) more lucrative to start your own ML company and hope for an acquisition than to do the work for other companies. We tried to engage with several small ML vendors in our space and more than half of them came back with suggestions that we simply acquire them for large sums of money. Meanwhile, one of the vendors we engaged with was acquired by someone else and, of course, their support dried up completely.

Ultimately we found a solution from a vendor that had prepared a nice solution for our exact problem.the contracts were drawn up in a way that wouldn’t be too disastrous if (when?) they were acquired.

I have to wonder if an industry-wide slowdown to the ML frenzy is exactly what we need to give people and companies time to focus on solving real problems instead of just chasing easy money.

The graph means very little without a comparison line of “all programming jobs” and/or “all jobs.”

> or have so little business value that they're not worth pursuing

It seems that I'm inverted from you. The Machine part of Machine Learning is likely of high business value, but the Learning part is the easier and better solution.

We do a lot of hardware stuff and our customers are, well let's just say they could use some re-training. Think not putting ink in the printer and then complaining about it. Only much more expensive. Because the details get murky (and legal-y and regulation-y) very quickly, we're forced to do ML on the products to 'assist' our users [0]. But in the end, the easiest solution is to have better users.

[0] Yes, UX, training, education, etc. We've tried, spent a lot of money on it. It doesn't help.