The gap between a working ML prototype and a production ML system is larger than most organizations expect. Surveys consistently find that only 20–25% of ML models ever make it to production — and of those that do, many fail to deliver their intended business value due to model drift, integration challenges, or operational failures.
What MLOps Actually Is
MLOps (Machine Learning Operations) is the set of practices, tools, and cultural norms that enable organizations to reliably build, deploy, and operate ML models at scale. It borrows from DevOps principles but addresses ML-specific challenges: data versioning (models are only as good as the data they're trained on), experiment tracking (reproducibility in ML is hard), model deployment (serving infrastructure differs from application infrastructure), and model monitoring (models degrade in ways that applications don't).