How to Grow Neat Software Architecture out of Jupyter Notebooks

Demonstrates how to write tests first in notebook cells to improve design upfront, as shown in the section on coding assertions before logic to enforce TDD principles.

Provides a step-by-step strategy for moving logic to external Python files, using real examples from machine learning projects like seq2seq-signal-prediction to ensure reusability.

Discusses growing layered architectures such as Domain-Driven Design or Clean Architecture from notebooks, based on the author's experience in consulting projects like document clustering.

Advises using notebooks primarily for visualization after extracting core logic and creating visualization tests, with examples from deep learning performance monitoring notebooks.