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AI/ML world is like JavaScript world, in the sense that there are so many new tech/tools that I can't make heads or tails. This is good thing btw.

But for someone who wants to jump the bandwagon, does anyone have a "guide/map"? To put it simply, "How do I start AI/ML in 2023? And then what?"

The "2023" part is important. If you're bringing someone new to JS world, you probably show them Vue/React and not jQuery/Prototype.

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It might look like that from the outside, but in practice I'd say even in 2023 most things are done with PyTorch/TensorFlow on Nvidia GPUs.

When you see a new compiler/runtime, it usually impacts deployment and optimization post training (TensorRT, FasterTransformers, TVM, OpenXLA) and/or target new specialized silicon (AWS Inferentia and Trainium, Google's TPUs, and others).

So to answer succinctly:

> How do I start AI/ML in 2023? And then what?

You implement your model in PyTorch, you train on Nvidia A100 and you deploy with the framework that gives you the best speedup for your architecture.

It also depends on what your trying to do.

if your just trying to learn the "basics" I would argue use google colab or something similar to get your feet wet. it's also a good way to learn python

Also you might look at huggingface or model zoo for existing models. It feels alot like early does of public apis. Where you can do alot of cool things with apis and mashups.

Useful thread from last year "Ask HN: In 2022, what is the proper way to get into machine/deep learning?" https://news.ycombinator.com/item?id=32480009

Edit: I would personally suggest reviewing the basics: linear algebra, optimization, then the classical ML technique. Then move on to deep learning. Look at important research papers and GitHub code, and implement models to really understand how they work.