Isn't it extensively used in robotics??

For simple NER tasks some simpler models might work too,like conditional random fields. The crfsuite package has a very easy to use implementation of it and it is using a C lib under the hood for the model training.

Yeah, I would try FastText before LLMs.

Meh, I'm sure some would say society is in a decline since pop culture appeared.

I think nowadays he is mostly known by music nerds, boomers and hip hop producers.

This! Online learning is a very common term that is used in time series modeling, for example in anomaly or change point detection.

Mostly implementing change point detection algorithms that in some way utilize ordinal pattern analysis.

No, it's not like that at all. IMO that analogy does not make any sense. First, r&d is not just thinking up stuff and then making them. In drug discovery it involves expensive trials. In other fields it may involve a lot of building and scraping things, sometimes from expensive material. Patent should be an incentive to do all that knowing that once that it's done it can monetized in a way that does not allow other companies just to copy and paste without effort. Should they be able to do it for everything and forever? Probably not and that is what I was referring to.

I dunno if a company paid for r&d then they should be entitled to exclusively make money from it for some time. I think the problem is around the what and the when and not the why.

GridSearchCV?

On that note there is a library that extends xgboost's parameters with survival analysis capabilities specifically. Here is a tutorial: https://loft-br.github.io/xgboost-survival-embeddings/how_xgbse_works.html

This. I have the same setup of laptop + work station. It's been great. No model training kills my laptop anymore. More memory for Chrome.

Theoretically yes, but it sounds like it requires more coding skills since you need to create the network structure by hand instead of relying on solutions that were specifically built for this. Don't get me wrong, there is nothing bad with what you described, but sounds to me OP needs a tool out of the box.

This already looks over complicated compared to the simply deployable and explainable alternatives that have been proven over time to be better at this problem for small datasets.

Is it though? One thing that comes back up again is the comparison to human learning. Do humans get clean training samples? I like to think not that. Instead humans learn how to separate signal from noise much better, and also learn how to model hidden causes.

How does it compare to filprofiler? Apart from the IDE integration of course. Does it work with parallelized or threaded code? Threading support is especially interesting for numpy.

https://github.com/pythonspeed/filprofiler

Foundations of Data Science is very good if you are looking for something that is pure math/stats

https://www.cambridge.org/core/books/foundations-of-data-science/6A43CE830DE83BED6CC5171E62B0AA9E

In my experience it's much easier to serve models through either AWS Lambda with containers or just upload your model in a container to an EC2 instance.

If your graphs are usually small then you might want to look into Graph Kernels: https://ysig.github.io/GraKeL/0.1a8/documentation/introduction.html

IMHO low code is fine for not resource intensive tasks. Once an analysts (without too much programming skills) starts running into performance or memory problems then that person will have a hard time adjusting their pipeline because the low code tools usually offer little flexibility.