Professional AI Project Directory Structure

A pile of notebooks named final.ipynb, final2.ipynb, and really_final.ipynb is how AI projects die. A consistent directory structure makes projects readable, reproducible, and collaboration-friendly.

This lesson covers the widely-used Cookiecutter Data Science layout.

Separate data by processing stage so raw inputs are never overwritten:

• data/raw/ — original, immutable source data
• data/processed/ — cleaned, model-ready data
• data/interim/ — intermediate transformations

Treat data/raw as read-only — you should always be able to regenerate everything else from it.