I've actually done a fair bit of ML work in Elixir, in practice I found:

1) It's generally harder to interface with existing libraries and models (example: whisperX [0] is a library that combines generic whisper speech recognition models with some additional tools like discrete-time-warping to create a transcription with more accurate time stamp alignment - something that was very helpful when generating subtitles. But because most of this logic just lives in the python library, using this in Elixir requires writing a lot more tooling around the existing bumblebee whisper implementation [1]).

but,

2) It's way easier to ship models I built and trained entirely with Elixir's ML ecosystem - EXLA, NX, Bumblebee. I trained a few models doing basic visual recognition tasks (detecting scene transitions, credits, title cards, etc), using the existing CLIP model as a visual frontend and then training a small classifier on the output of CLIP. It was pretty straightforward to do with Elixir, and I love that I can run the same exact code on my laptop and server without dealing with lots of dependencies and environment issues.

Livebook is also incredibly nice, my typical workflow has become prototyping things in Livebook with some custom visualization tools that I made and then just connecting to a livebook instance running on EC2 to do the actual training run. From there shipping and using the model is seamless, and I just publish the wrapping module as a library on our corporate github, which lets anyone else import it straight into livebook and use it.

[0] https://github.com/m-bain/whisperX

[1] https://hexdocs.pm/bumblebee/Bumblebee.Audio.Whisper.html