It's not so perplexing when you understand that Python has long had the best ecosystem of libraries for data science and ML, from which the current wave of AI stuff was born. There are plenty of reasons to dunk on Python, but the reality is lots of people were getting real work done with it in the run up to where we are today.
Yes, today’s ML engineer has practically no choice but to use Python, in a variety of settings, if they want to be able to work with others, access the labor market without it being an uphill battle, and most especially if they want to study AI / ML at a university.
But there were also the choices to initially build out that ecosystem in Python and to always teach AI / ML in Python. They made sense logistically, since universities largely only teach Python, so it was a lowest-common-denominator language that allowed the universities to give AI / ML research opportunities to everyone, with absolutely no gatekeeping and with a steadfast spirit of friendly inclusion (sorry, couldn’t resist the sarcastic tangent). I can’t blame them for working with what they had.
But now that the techniques have grown up and graduated to form multibillion-dollar companies, I’m hopeful that industry will take up the mantle to develop an ecosystem that’s better suited for production and for modern software engineering.
When it comes to modern Python, the only thing that can make it not production-ready is it being slow. Given that people in machine learning are using Python as a glue language for AI/ML libraries, this negligibly impacts their workflow.
How good is JS interop with C/C++/BLAS? That's the basic stepping stone, I think. If you cannot make something in JavaScript that can compete with numpy there's little chance that things will change anytime soon.
I don’t know the details as specifically, since I haven’t been able to justify investing my efforts in the non-flagship ecosystem within the TensorFlow project after it previously added its Swift version to the Google Graveyard, but TensorFlow.js is doing something in this direction for the Node.js version. This info is at: https://www.tensorflow.org/js/guide/nodejs
“Like the CPU package, the module is accelerated by the TensorFlow C binary. But the GPU package runs tensor operations on the GPU with CUDA.”
They note that these operations are synchronous, so using them will sacrifice some of JavaScript’s effectiveness at asynchronous event processing. This is not different from Python when you are training or serving a model. JavaScript’s strengths would shine brighter when coordinating agents / building systems that coordinate models.
