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The original was posted on /r/machinelearning by /u/Wise_Panda_7259 on 2025-01-18 21:18:11+00:00.

I do a lot of experimentation in Jupyter notebooks, and for most projects, I end up with multiple notebooks: one for EDA, one for data transformations, and several for different experiments. This workflow works great until it’s time to take the model to production.

At that point I have to take all the code from my notebooks and refactor for production. This can take weeks sometimes. It feels like I’m duplicating effort and losing momentum.

Is there something I’m missing that I could be using to make my life easier? Or is this a problem y’all have too?

*Not a huge fan of nbdev because it presupposes a particular structure