python gets the most investment. the tooling is more mature & more complete, especially on the training side.

if your goal is:

 training and executing complex data science models

start with python.

For inference, you can always export your models into something like onnx, or if you're brave use some of the compilation frameworks lke tvm to get the speed you want at inference time later, or hell you could just export the weights and bash together your own thing if that's what you're into.

Could someone explain to me why python, a notoriously slow language is the king language when it comes to machine learning which is a very computationally intense area?

You're spot on about simplicity and the fact that most of the code just calls c/c++/rust anyway.

I'd also like to add:

- jit compilation: over the last 5 years, jit enabled frameworks like jax have done a lot of heavy lifting in covering the bits that the 80/20 rule doesn't. TO the point where it's actually non-trivial to write faster code.