I love Rye. After using package managers from other languages like cargo and hex, the lack of a similar system for Python always had me yearning for more. I'm really happy to say Rye has completely solved this itch for me, and its a real pleasure to use as someone who doesn't want to install different tools to manage venvs, python versions and my project's dependencies. Rye just does it all.
I haven't had an idea of Rye yet, but conda can do "manage venvs, python versions and my project's dependencies" fine.
I've settled on just using Poetry for most things, and then using pip in a venv to install the pyproject.toml file from Poetry either in a Dockerfile or directly on a cluster. That's worked fairly well so far, even with torch/cuda (and the mess of CUDA versioning) and from macOS to Linux.
I think uv/rye is a good next step, Poetry can be a bit slow as well at times.
Maybe it's different for other ecosystem such as node etc., but when I'm doing research in ML I config my project mostly just once and do the bulk work (install cuda pytorch etc.), later it's mostly just activate and occasionally add some util packages via pip.
What makes conda better than native venv+pip is its extensive libraries/channel and be able to solve/build complicated dependencies effortlessly especially when you have to run your project on both Windows and Linux.
This is not to say speeding up isn't needed, of course!
For me, most stuff is installed via pip anyways. The only things I'm pulling via conda is blas, scipy, torch and all that stuff that's a PITA to install.