Deployment
Deployment into production is often seen as separate from the creation of models. At many companies, data scientists create models in isolated development environments on training, validation, and testing data that was collected to create models.
Once the model performs well on the test set, it then gets passed on to deployment engineers, who know little about how and why the model works the way it does. This is a mistake. After all, you are developing models to use them, not for the fun of developing them.
Models tend to perform worse over time for several reasons. The world changes, so the data you trained on might no longer represent the real world. Your model might rely on the outputs of some other systems that are subject to change. There might be unintended side effects and weaknesses of your model that only show with extended usage. Your model might influence the world that it tries to model. Model decay describes how models have a lifespan after which performance deteriorates...
