[rm999]

Python did not win the ML language wars because of anything to do with front-end, but rather because it does both scripting and software engineering well enough. ML usually requires an exploration/research (scripting) stage and a production (software engineering) stage, and Python combines these seamlessly better than many ML languages before it (Matlab, Java, R). Notebooks became the de facto frontend of ML Python development and to me it's evidence that frontend in ML is inherently messy.

Do I wish a better language like Julia had won out? Sure, but it came out 10+ years into this modern age of ML, which is an eternity in computing. By the time it really gained traction it was too late.

[freilanzer]

> because it does both scripting and software engineering well enough

It certainly does scripting decently, but for software engineering it's hell.

[rm999]

I agree, but can you imagine that Matlab, and then R, were the de facto ML languages before Python really took off? Putting R models into production was an absolute nightmare. Before R, I was writing bash scripts which called Perl scripts that loaded data and called C code that loaded and ran models that were custom built by Matlab and C. Python (and the resulting software ML ecosystem) was a huge breath of fresh air.

[avinassh]

I agree with you. I also find Python to be slower for iterations and refactoring.

I ranted on about it recently - https://avi.im/blag/2023/refactoring-python/