Why Good Models Fail After Deployment

“Why Good Models Fail After Deployment” explores a paradox familiar to many ML practitioners: models that perform exceptionally well during training and offline evaluation often degrade—or even cause harm—once deployed in real-world systems. This talk examines structural reasons behind this phenomenon, focusing not on model architectures, but on the environments in which models operate.