[braza]

> Too many fractured packaging solutions, not a single one well designed. And they all conflict.

100% this.

Last 4 years, one of the most frustrating parts of SWE that I need to deal with on a daily basis is packaging data science & machine learning applications and APIs in Python.

Maybe this is a very mid-solution, but one solution that I found was to use dockerized local environments with all dependencies pinned via Poetry [1]. The start setup is not easy, but now using some other Make file, it's something that I take only 4 hours with a DS to explain and run together and save tons of hours of in debugging and dependency conflict.

> Python developers would rather save 1 line of code in the moment, even if it pushes the complexity off to another part of the system.

Sounds odd to me in several projects that I worked on that folks bring the entire dependency on Scikit-Learn due to the train_test_split function [2] because the team thought that it would be simpler and easier to write a function that splits the dataset.

[1] - https://github.com/orgs/python-poetry/discussions/1879 [2] - https://scikit-learn.org/1.5/modules/generated/sklearn.model...

[biztos]

I'm trying to do the same but with uv instead of poetry. So far so good, and it helps that for me delivering as a docker container is a requirement, but I have no idea what's going to happen if I need to run "real" ML stuff. (Just doing a lot of plotting so far.)