Inherited an ML codebase last year.

47 Jupyter notebooks. No version control. Model weights stored in a shared Google Drive folder named "models_final_v3_USE_THIS_ONE."

The previous engineer had left. Nobody else understood the system. The model was running in production, making real decisions, and nobody could explain how it worked or reproduce the results.

This is more common than anyone admits.

It took me three weeks just to understand what the system was doing. Another two weeks to create a reproducible training pipeline. Two more weeks to set up proper version control, testing, and monitoring.

Seven weeks of engineering work before I could make a single improvement to the actual model.

This is the hidden cost of skipping MLOps. Not the cost you pay today — the cost your successor pays in six months when they inherit your mess.

Every hour you spend on clean code, documentation, and reproducible pipelines saves your future self (or the poor person who comes after you) ten hours of archaeological excavation.

Write code like the next person to read it is a sleep-deprived version of you at 2 AM. Because it might be.