Most Machine Learning models never reach production, and of those that do, many degrade silently. MLOps brings engineering practices to the model lifecycle.
The essentials
Versioned model registry, automated deployment, and monitoring of data and performance drift. Without this, a model that was good in training becomes a risk in production.
Starting simple and iterating is better than attempting the perfect platform on the first try.
