There's a famous paper from Google called "Machine Learning: The High Interest Credit Card of…

Technical Debt." It was published in 2015 and somehow it's even more relevant in 2026.

ML technical debt is worse than software technical debt because it's invisible. Your model doesn't throw errors — it just quietly gets worse. Your features become entangled in ways nobody understands. Your data dependencies are undocumented. Your pipeline is a jungle of ad-hoc scripts that only one person (who left six months ago) understood.

I've inherited ML projects that looked clean on the surface and were absolute disasters underneath. Stale models nobody updated. Features computed differently in training vs. serving. No monitoring. No documentation. No tests.

How I manage it now: documentation for every model (not optional — required). Automated tests for data quality. Regular model audits (quarterly minimum). Clean, modular feature engineering pipelines. Proactive sunsetting of unused models.

The rule I enforce on my teams: 20% of every sprint goes to paying down technical debt. It feels expensive until you compare it to the cost of a production outage caused by a model nobody understood.

Technical debt kills ML projects slowly. Pay it down consistently or it will bankrupt you all at once.