What is your ML model supposed to do?

> Can I spend $1 million on cloud computing companies ... (AWS, Azure, Google Cloud)

Yes, any one of these companies will gladly accept money for the services they provide.

> and would that make sense?

Depends on the problem you're trying to solve, most applications don't need that kind of budget. If you need to ask, you probably don't need to spend that much.

Why would RL be doomed? Didn’t sticking RL on top of a big GPT model just give us ChatGPT?

Python if you want to get actual work done.

Swift for ML isn’t a thing - it was pushed by only a couple of people for a very short period of time a while ago now, and they gave up on it pretty quickly.

Galactica was doomed to fail because it was specifically marketed as a science tool which puts very high expectations on factual and mathematical correctness. ChatGPT on the other hand is marketed as chat.

Do the one you're more interested in, you won't be more or less imployable because of an optional university course.

There’s off the shelf stuff now and we have easy enough model API for a bunch of use cases. I don’t know what you mean expect LLMs to change - be a better autocomplete or better search? Maybe but it doesn’t seem like a fundamental change.

For some problems like tabular data or time series there are plenty of alternatives, some of them better than deep learning (for a specific application). But those are very different from the cutting edge vision/language models.

It’s easier than the alternatives. If you don’t think it is, use whatever you think is better. You’ll either solve your problem and find that better language or you’ll learn why Python is used so much.

What about point 1? Did you try keeping the pretrained model frozen?

1. Try without any fine tuning, use the pretrained network as a preprocessing step.

2. Try a different/newer model.

Twitter and Meta are the most visible examples but they aren’t outliers as far as layoffs go.

It’s normal to have more layoffs, many ML jobs aren’t essential. We’re still hiring but most people we’re interviewing have been recently laid from somewhere so the market has definitely been impacted.

No, you don’t. You’re a beginner - from a time/effort investment perspective learning C++ (and especially Rust) is a big distraction. You have limited bandwidth - focus it on ML theory, Python and the Python ML ecosystem.

DeepMind obviously.

> Assuming similar total compensation

This is a bad assumption, startups don't pay that well.

> if the startup succeeds then you get to be rich when the equity/options skyrocket.

This is the kind of crap they tell you to get away with giving you a lower salary.

You need to be pretty gullible to think that this is a close decision.

> Low code ML tools offer a bigger population within an organization to take part in ML development

Nope.

> while allowing flexible customization for the technical user.

Not at all.

So overall I would say no.

Seriously, who are the people in your organisation who know enough to work in ML but not enough to write code? (that's who the first part is supposedly enabling). And what no code tools give you better customisation than a programming language? (that claim is basically marketing nonsense)

Did you just make something up and ask us to explain it to you?