[ogarten]

This is a broad question.

AI/ML needs a math-heavy background and also a lot of domain knowledge to make any sense.

I think the best path for software engineers to go into data is via data engineering or MLOps or whatever it is called tomorrow. This is relatively close to software engineering in many cases, however it's becoming more and more about writing YAML files, too.

What these field could really benefit from though is some software engineering best practices such as testing, linting, formatting and so on.

[AznHisoka]

You don’t need a math heavy background to do most ML/AI tasks. In fact I would say you don’t need much of a prior background. Just a desire to learn, tinker and experiment, just like most of software engineering

[ogarten]

Mh, I guess you got a point. I still stand with what I said more or less though.

While you don‘t always need formal maths it gets much easier when you have a formal education in the field.

[aliston]

There's also a huge difference between someone who is working as an ML researcher versus someone working on AI infra or AI-based feature development.