@Tobias Cortese​, the value added to use ML runtime is in a fact that the Databricks Runtime ML includes a variety of popular ML libraries that are updated with each release to include new features and fixes.

In addition, Databricks has a subset of the supported libraries as top-tier libraries. For these libraries, Databricks provides a faster update cadence, updating to the latest package releases with each runtime release (barring dependency conflicts).

In addition to the pre-installed libraries, Databricks Runtime ML differs from Databricks Runtime in the cluster configuration and in how you manage Python packages.

Finally, ML runtime includes tools to automate the model development process and help you efficiently find the best performing model : AutoML, Managed MLFlow, Hyperopt.

Hope that you'll get ML runtime at your disposal