You have a working ML model in a notebook. Now what?
The gap between a prototype and a production ML system is where most AI projects fail. MLOps bridges that gap.
**What MLOps actually means**
MLOps applies DevOps principles ? automation, versioning, monitoring ? to machine learning. The goal is to deploy models reliably, track their performance and retrain them when they drift.
**The minimum viable MLOps stack**
*Model versioning:* Use MLflow or Weights & Biases to track experiments, param