Sandeep Pawar crosses workspaces:
I have written a couple of blogs about working with ML models in Microsoft Fabric. Creating experiments and logging and scoring models in Fabric is very easy, thanks to the built-in MLflow integration. However, the Fabric Data Science experience has one limitation. There are no model endpoints yet, and you cannot load a model from another workspace because the model URI, unlike in Databricks, does not reference a workspace. If you use
MLFlowTransformeras shown in this blog, only the model from the workspace where the notebook is hosted is loaded. However, there is a workaround.
Read on for that workaround, as well as the core limitation associated with it.