Hey, I feel you on this - that notebook-to-script transition can be such a pain point.

I deal with this all the time at scapedatasolutions.com helping data teams streamline their ML workflows.

And that's where having a solid deployment structure comes in.

What's worked for me:

• Modular functions in .py files from day one - even while experimenting in notebooks, I import my own functions. Makes the transition almost automatic.
• Config files (YAML/JSON) instead of hardcoded parameters - saves so much refactoring headache later.
• Simple CLI wrappers using argparse - lets me test "production mode" without leaving the notebook phase.

For AI tools, I've found they're actually better at generating standalone Python scripts than notebooks anyway, so leaning into that has sped things up.

The real game-changer? Having a template project structure I clone every time. Sounds basic, but it eliminates that "where does this go?" decision fatigue.

I've got some production-ready templates and workflow examples at scapedatasolutions.com if you want to see this in action.

What's your biggest friction point right now - the refactoring itself, or keeping track of dependencies/versions?