Many machine learning models perform great during training—but start failing once they reach production. From my recent learning in MLOps and AI testing, I’ve realized that the issue isn’t usually the model itself. It’s the lack of operational practices like monitoring, drift detection, safe deployments, and retraining. I wrote a short post explaining: why ML models degrade in production how data