Its quite simple to develop a model, bundle it up for deployment and deploy. Nobody cares about your fancy YAML based containerized deployment and monitoring setup, everyone has that. The challenge comes in when you have a continuous cycle of data ingestion to model optimization, training, evaluation and deployment. Pretty much everybody has huge amount of code duplication in there. It also comes from the fact that ml researchers are barely capable of programming a light switch, like how are you ever gonna put the horrible trash of code they ducked tape together from medium posts into a production environment. Hopeless.