Conda is much, much better for the C/Fortran/C++ parts of data science/machine learning workloads.

Like, I had real issues with GDAL and SQLite/spatialite on MacOS (easy on Linux) which uv was of no help with. I ended up re-architecting rather than go back to conda as uv is just that good (and it was early stage so wasn't that big of a deal).

I stay with it because last time I tried uv it was still directory/project focused vs. environment. With conda it doesn't matter where I am, I can activate any of my environments and run code

cuda-nvcc was a blocker for us but it looks like a stable version is coming to pypi