Discovering the model registry

Models is a fully managed and integrated MLflow model registry available to each deployed Databricks ML workspace. The registry has its own set of APIs and a UI to collaborate with data scientists across the organization and fully manage the MLflow model. Data scientists and ML engineers can develop models in any of the supported ML frameworks (https://mlflow.org/docs/latest/models.html#built-in-model-flavors) and package them in a generic MLfLow model format:

Figure 2.15 – The Models tab

The model registry provides features to manage the versioning, tagging, and state transitioning between different environments (moving models from staging to production to archive):

Figure 2.16 – The Registered Models tab

Before we move on, there is another important feature that we need to understand: the Libraries feature of Databricks. This feature allows users to utilize third-party or custom...