MLOps: Keeping a Model Working After You Ship It

Training a model is the part that works. The system around it is what decays, and it decays quietly: no exception, no alert, just answers that are slowly less right. This path covers what actually breaks. Why changing one feature moves every weight, the three levels of automation and which one you need, the skew between training and serving that no isolated test can see, and the monitoring that decides when a model has stopped earning its place.