Setting up a full-fledged local MLflow tracking server

In Chapter 2, Getting Started with MLflow for Deep Learning, we gained hands-on experience working with a local filesystem-based MLflow tracking server and inspecting the components of the MLflow experiment. However, there are limitations with a default local filesystem-based MLflow server as the model registry functionality is not available. The benefit of having a model registry is that we can register the model, version control the model, and prepare for model deployment into production. Therefore, this model registry will bridge the gap between offline experimentation and an online deployment production scenario. Thus, we need a full-fledged MLflow tracking server with the following stores to track the complete life cycle of a model:

• Backend store: A relational database backend is needed to support MLflow's storage of metadata (metrics, parameters, and many others) about the experiment. This also allows the query...