the main reference that shows up if you search for erlang + hpc is from 2008
https://www.researchgate.net/publication/221211398_High-perf...
I'm a Elixir and Erlang developer who is just starting to explore ML, but these libraries seem solid and well designed.
There is also the NX project (numerical elixir) which compiles tensors to GPU code making it much faster for those particular usecases. https://github.com/elixir-nx This has things like explorer which is elixir bindings to polars, livebook which is like jupyter notebooks (with enforced execution order thang god), aswell as some machine learning libraries in axon and scholar.
There has also been some work with libraries like flow and broadway which allow pipelines to be constructed with the usual syntaxt `step1 |> step2 |> step3` but to be executed across cores (or cluster).
I can imagine orgs using a combination of the above tools to do some interested distrubuted data pipelining and ML.