Why I Stopped Using Notebooks for Production ML

Jupyter notebooks are fantastic for exploration and prototyping. But when it comes to production machine learning, they introduce a host of problems that make them more trouble than they're worth.

The Hidden State Problem

Notebooks maintain hidden state. You can run cells out of order, delete cells that defined variables, and end up with a notebook that works on your machine but fails everywhere else.

Testing is an Afterthought

Try writing unit tests for notebook code. It's possible, but it's painful. The notebook format wasn't designed for testability, and it shows.

What I Use Instead

I've moved to a workflow that combines:

• Python modules for all core logic
• Pytest for testing
• Hydra for configuration management
• DVC for data versioning

The result is ML code that's reproducible, testable, and deployable.