Is there a concrete reason for that? A lot of the approach in Elixir is to not go the route of other languages and merely slap an adapter interface on Python libraries. The approach is to approach things from a very Elixir and BEAM specific point of view. This is how Livebook was developed and how the machine learning libraries are being developed.
In fact, one could view it as Python being the one that's going to struggle to overcome the limitations set forth by its own language design. Elixir comes with very tightly integrated tooling (projects, testing, type analysis, static analysis, notebooks, scripting, etc.) that basically has 100% adoption rate without a plethora of third-party competitors and lack of integration with the language such as Python has. With immutability and concurrency built into the language, Elixir has a leg up, and there is active research into bringing static typing to Elixir and also binding Elixir to faster runtime environments, such as Rust.
Python simply cannot make the jump to Elixir, Erlang, and BEAM's language and VM capabilities like they can to a machine learning ecosystem akin to Python's.