Because data changes over time, even in productive ML settings, we are pretty much constantly in a loop of collecting data, exploring models, refining models, and finally testing/evaluating, deploying, and in the end monitoring our models
https://towardsdatascience.com/model-management-in-productive-ml-software-110d2d2cb456
ModelDB: A System to Manage Machine Learning Models
https://databricks.com/session/modeldb-a-system-to-manage-machine-learning-models
MLflow: A platform for managing the machine learning lifecycle
https://www.oreilly.com/content/mlflow-a-platform-for-managing-the-machine-learning-lifecycle/
