Managing machine learning models

As we have seen before, in Azure Databricks we have at our disposal the MLflow Model Registry, which is an open source platform for managing the complete lifecycle of a machine learning or deep learning model. It allows us to directly manage models with a chronological linage, model versioning, and stage transition. It provides us with tools such as Experiments and Runs, which allow us to quickly visualize the results of training runs and hyperparameter optimization, and to maintain a proper model version control to keep track of which models we have available for serving and quickly update the current version if necessary.

MLflow has in Azure Databricks a Model Repository user interface (UI) in which we can set our models to respond to REST API requests for inference, transition models between stages, and visualize metrics and unstructured data associated with the models, such as description and comments. It gives us the possibility of managing...