[pjmlp]

Rust might not be it.

But AOT/JIT compiled languages that can naturally talk to the GPGPU, without 2nd language syndrome, like Julia, Swift, Java and .NET will certainly be more attractive to data science practitioners.

I can already envision those life science guys that migrate to VB.NET when they have outgrown their Excel/VBA code, to start playing with ML.NET.

[kuzehanka]

Python already gets JIT compiled to CUDA[1] and there's an entire funded ecosystem built around python+gpgpu called RAPIDS[2] which is the future of the ML space by most indicators.

I don't see any other language even making a dent in the Python ecosystem without some kind of new killer feature that can't be quickly replicated in Python.

[1] https://numba.pydata.org [2] https://rapids.ai

[hsaliak]

You beat me to making this point.

I don't see anything in the article's python code that the numba's jit decorator cannot handle. When numba works (it's rapidly improving), it's seriously impressive.

For this particular case, you should be able to get really good performance without sacrificing readability.

Also Jax - https://github.com/google/jax

[MiroF]

Swift and static type checking and compiler analysis might be that language and feature combo.

[disgruntledphd2]

Thank you for the cite to rapids.ai, that looks extremely interesting :)

[MiroF]

Your last paragraph is my nightmare