Why ML Models Break After Deployment

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 and concept drift impact predictions where MLOps and QA make a real difference Read the full article