this post was submitted on 20 May 2025
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as someone fairly new to larger python projects, working in a team too - why does it seem like I’m always in dependency hell? Sometimes things work with venv until I forget and a notebook needs something else. Or a diff type of proj because mlflow wanted another kind of env manager?
That's one of the main pain points of Python. Something like poetry or uv can help but the python ecosystem is just unstable. It's very hard to update a dependancy. You project might only run with very specific versions of libraries and the language itself.
I honestly hate that about python and I don't think python is good for developing large software. Even js/ts is a much better ecosystem. (I'm bracing for the hate)
hrm. yeah. I am “trying on” some basic ml models (just simple classifications) into our workflow and while I can whip one up with scikit, a notebook, and serve it with flask/fast - there is no way I can build a workflow tenable for others who are more “data analytics” than coders. The ecosystems for low/no code model generation (knime etc) with handoff to engineers like me to serve is … young.