Each library has been building on top of the previous libraries & abstractions (including transpiling Elixir instructions into GPU code, see "defn" etc).
Since you mention Erlang, there is even a Machine Learning Working Group at https://erlef.org/wg/machine-learning.
The most iconic & advertised case I'm aware of is the work done at Amplified https://www.amplified.ai ; this has been the topic of a Keynote at ElixirConf EU this year, which you can find at https://www.youtube.com/watch?v=5FlZHkc4Mq4.
I am also starting to use ML + Elixir in production and I'm aware of other individuals doing so.
I do not have a registry of companies doing so, but we're seeing more and more experienced ML practitioners mentioning they are coming from Python and willing to try something different (e.g. https://elixirforum.com/t/data-science-and-machine-learning-... and other posts on Elixir Forum).
Hope this helps!
- https://github.com/elixir-nx/axon Nx-powered Neural Networks
- https://github.com/elixir-nx/nx Multi-dimensional arrays (tensors) and numerical definitions for Elixir
- https://github.com/elixir-nx/scholar Traditional machine learning on top of Nx
- https://github.com/elixir-nx/bumblebee Pre-trained Neural Network models in Axon (+ Models integration)
- https://github.com/elixir-explorer/explorer Series (one-dimensional) and dataframes (two-dimensional) for fast and elegant data exploration in Elixir
- https://fly.io/blog/rethinking-serverless-with-flame/ (for offloading large work to remote containers)
- https://www.youtube.com/watch?v=RABXu7zqnT0 InstructorEx
And of course Livebook (https://livebook.dev)
Old but interesting video https://www.youtube.com/watch?v=g3oyh3g1AtQ (Bumblebee: GPT2, Stable Diffusion, and more in Elixir)
A talk on the upcoming Elixir conference (https://2024.elixirconf.com/schedule/#schedules) is actually titled "Livebook in the cloud: GPUs and clustered workflows in seconds".