Instead of moving to Elixir I believe it makes more sense to wait/move to Mojo when it's ready:
Throwing BEAM or FP acronyms around won't really strike a chord with people working with data and models.
Mojo will (as per promise) tap into the wider ecosystem. Other platforms are more than welcome to try but this ultimately requires a huge community of scientists / developers to become a real alternative.
Other languages have certain features that make extension and integration feel like first-class concerns which lowers the barrier to contributions from a wider range of people and also helps keep e.g. dependencies and build processes relatively simple.
Python was not designed for ML, it happened to it, the way Android happened to Java etc. Loosely speaking the Mojo project serves a function similar to that of Kotlin in the Android mobile world. Trying to remedy some recognized friction points while maintaining the benefits of a widely established ecosystem.
Obviously not holding a crystal ball: if the ML hype mutates into something more permanent and very widely embedded across different verticals (not just the big tech sponsored pytorch / tensorflow platforms and use cases) and if the Python/C++ combo becomes a recognized bottleneck then the conditions might spark another approach.
Elixir & Python are not an apples to apples comparison - there are fundamental differences in the programming model (functional, immutability, etc) and runtime (preemptive scheduling + OTP) that is the reason it has distinct advantages not available without heavy cost trade-offs elsewhere.
Either way once Mojo is production ready Elixir will be able to use it as well like it does Rust, Zig, or Python.
Last week heard a story about an ML dev that would literally rebuild his system every week because python would break it