I've created a Databricks Model Serving Endpoint which serves an MLFlow Pyfunc model. The model uses langchain and I'm using mlflow.langchain.autolog().

At my company we have some production(-like) workspaces where users cannot e.g. run Notebooks and everything is done my Service Principals (e.g. in Jobs).

In those workspaces, I cannot use the detailed trace view to inspect my MLFlow traces. I can see a list of traces in the Experiments UI

but when I click on an individual trace, I get something like

and not the clean Langchain detailed view like

Interestingly, if I use another workspace (where human users have more access) to `display` the Inference Table in a Notebook similar to [this](https://docs.databricks.com/aws/en/mlflow3/genai/tracing/observe-with-traces/ui-traces#tracing-in-da...), I can access this nice detailed view.

Is there a workspace setting (or access to compute resources) that is necessary in order to display the Detailed Trace view?