My company has a product which uses ML to solve a fairly valuable problem. I would not at all call the ML very advanced.

It takes a layered approach where it uses more than one ML model after another to accomplish the task.

No PhDs are going to be earned from this; but it does solve the problem very very very well.

What is super annoying is the class of company which needs this solution is fairly large. Typically 5000-50000 employees. This means they almost certainly have a "data science" group, often 20+ people. All PhDs. All. Usually Math, stats, "data science", or ML if the are a recent hire.

In exactly zero cases have any of these groups produced a product which went into real time production. A few of them have a few jupyter notebooks where they take some data, screw with it, and then return a vaguely useful report. But nothing live like our product producing value in real time.

Our engagements with these companies are almost identical every time. We talk to someone in upper management. They get excited about our product. We give a few demos of it working very well.

Then they get their "data science" group involved and they want to do two things:

• Get a copy of our models,
• And shut us out.

There is exactly a zero percent chance we will have any progress after meeting with their data science people. Often the conversations are bizzare. They ask for our models. We say, "No, that is how we make money." They ask a few different ways. Then they start dropping off the video call, and the entire thing just dies.

Where we have had more success is to just put our foot down. When they say that they want their "data science" people to talk to us, we say, "Well it was nice knowing you. Bye bye." They say, "Wait what?" and we explain, "Look, those academics are going to say two things, "What are your models?" and then after the call they are going to say we don't have the credentials to do this kind of work because we don't have PhDs.

So, we aren't interested in wasting any more time with this company.

They get mildly defensive about their ML people and we say, "We aren't interested in being shut down by a group of academics who probably haven't produces squat in the last 5 years."

They then say something like, "No, they are a huge cost center producing nothing. We are hoping you can work with them." We reply, they don't want to work with us, we are inferiors and we will also make them irrelevant.

We leave it at that, and often the engagement continues with the exeuctives making fun of how useless their "data scientists" are.

I've been putting their title in quotes because anything which puts science in its title isn't a science at all.

And this last is where academics fail hard at most practical ML. They are generally terrible programmers not good at solving problems. Problem solving is an art. The more academic knowledge you have can be a help to your problem solving skills, but only if you have any.

It seems that the people I hear of who are kicking ass and taking names at places like deepminds, etc, are both. Highly skilled problem solving programmers, and also highly knowledgeable academics.

The reality of ML is that there are lots of tools and libraries available to non academic programmers that this sort of thing is not very hard anymore. There are very few areas in the real world which require highly esoteric academic knowledge to solve the problem.

Yet, I see companies where they even snobbishly try to say there are ML engineers, and "Data Scientists" in an attempt to maintain their lofty status.

Here is an example of just how crappy the sort of PhD ML people I've dealt with are:

I gave them a one year data pull from a sensor database. The dates were in epoch seconds GMT (a standard in this particular industry), and the data was generated using a query where I used a range which resulted in the first second of the next year also being in the csv. So 31,536,001 rows of data instead of 31,536,000.

This whole team (about 8) were unable to deal with the dates, and were entirely flabbergasted by the extra row. They demanded I "fix" the dates, and that I give them the correct number of rows.

This was data for them to do R&D on, not feed into some already built system.

Think about that. 8 ML PhDs couldn't convert Unix dates or delete one row from a csv. WTF?

How are these fools going to properly clean up real world noisy sensor data which has all the wonders often found here. Dropouts, extreme outliers such as a pressure meters reading 12 million PSI, etc if they can't deal with an epoch second date format or an extra row. Also, there are subtleties with this sort of data they never asked about. Such as flow meters which get occasionally re-calibrated, which means there is both drift, and then sudden shifts in how these values will now relate to the system.

Oddly enough they never produced anything of value, other than some very significant billing.

And this is where another ML project failed like many many many that I have seen where way too many, way too "overqualified" people are given a task which is simply far beyond, not only their skill set, but often their basic problem solving aptitude.

It is far far far easier for a competent problem solving developer to learn enough ML to do very well, than for an ML academic to become a competent problem solving developer.