[notemaker]

I'd recommend moving to development that interfaces with ML instead of moving to being an ML practitioner. You have much faster feedback cycles, your work is predictable (engineering, not science), and you don't feel the pressure of never reading enough papers.

To do that you only need to understand the fundamentals of tensors, some basic knowledge on what the big no-nos are within ML development so you can course correct your peers if they break them, and either focus on the operations side of things or deployment. In both cases, having a knack for optimizing bottlenecks will be very helpful since they will be present during both training and inference.

[wmeredith]

As a designer/developer with a lot of expertise in UX and front-end technologies, I would love to go this route. Do you have any suggestions on where to start in terms of tutorials, tech, books, etc?

[notemaker]

Read Deep Learning with Python [1]. It's ok not to understand everything, but doing the labs will be invaluable.

That book should set you up with the fundamentals. Pytorch is the defacto standard right now for training; during deployment you will use whatever your deployment setup allows (e.g. embedded devices typically have their own inference frameworks).

Python is the lingua franca for anything in machine learning, but other languages are used where necessary for performance or for ecosystem benefits.

[1]: https://www.manning.com/books/deep-learning-with-python

[c0mbonat0r]

got any good resources you can share?