I'm an Applied Scientist vs. ML Engineer, if that matters.
Conda.. it interfere with OS setup and has not always the best utils. Like ffmpeg is compiled with limited options, probably due to licensing.
It causes so many entirely unnecessary issues. The conda developers are directly responsible for maybe a month of my wasted debugging time. At my last job one of our questions for helping debug client library issues was “are you using conda”. And if so we just would say we can’t help you. Luckily it was rare, but if conda was involved it was 100% conda fault somehow, and it was always a stupid decision they made that flew in the face of the rest of the python packaging community.
Data scientist python issues are often caused by them not taking the 1-3 days it takes to fully understand their tool chain. It’s genuinely quite difficult to fuck up if you take the time once to learn how it all works, where your putbon binaries are on your system etc. Maybe not the case 5 years ago. But today it’s pretty simple.
The interminable solves were awful. Mamba made it better, but can still be slow.
Plenty of more esoteric packages are on PyPI but not Conda. (Yes, you can install pip packages in a conda env file.)
Many packages have a default version and a conda-forge version; it's not always clear which you should use.
In Github CI, it takes extra time to install.
Upon installation it (by default) wants to mess around with your .bashrc and start every shell in a "base" environment.
It's operated by another company instead of the Python Software Foundation.
idk, none of these are deal-breakers, but I switched to venv and have not considered going back.