[trybackprop]

I actually wrote a blog post about this for experienced software engineers like you who are thinking of transitioning to ML, so I wanted to share it here: https://www.trybackprop.com/blog/2024_06_09_you_dont_need_a_...

I write about various engineers who now work at Meta, Google, Amazon, and OpenAI who made the switch. You can see what strategies and tactics they used to do it.

1) It's "wise" if you find during your personal hours you are enjoying hacking on it. Before I made the switch, I spent a year studying the material on nights and weekends so that was m my first data point that perhaps this is something I wanted to do full time.

2) Yes, I have! And I've been an ML engineer for 7 years now after I made the switch. For context, I'm an ML tech lead at FAANG. Prior to that, I worked in infrastructure and product.

3) One piece of advice I got on this years ago is to join a team adjacent to ML work so that you can get familiar with what production ML looks like. You can also start practicing ML thinking on Kaggle.com.

P.S. You can check out other posts in my blog for resources to learn AI/ML and the math needed for this career, such as my Linear Algebra 101 for AI/ML series: https://www.trybackprop.com/blog/linalg101/part_1_vectors_ma... (includes interactive quizzes, fundamentals of vectors/matrices, and a quick intro to PyTorch, an open source ML framework widely used in industry)

[vasili111]

How much math is truly necessary to work as MLE in a company where you do not need to write papers but need to deliver working ML systems?

[ngc248]

Just basic stats, basic calculus etc to working with data and use ML algos/ML techniques etc.