Up to 88% of corporate machine learning projects never make it to production. The models get built, they work brilliantly in the lab, and then they quietly die on a server somewhere. That failure rate isn't a talent problem. It's an infrastructure problem — and it spawned an entirely new discipline to solve it.

This episode breaks down MLOps, or machine learning operations, the invisible engine behind every AI system that actually works in the real world. The starting point is a 2015 paper titled "Hidden Technical Debt in Machine Learning Systems," which exposed a fundamental truth the industry didn't want to hear: building a predictive model is only a tiny fraction of the battle. The real challenge is sustaining it. Traditional software follows static logic — if X, do Y — and it stays that way until someone rewrites the code. Machine learn ...