Workspace advice for ML tasks

Hello,

I need some advice/example use case for using ML in Fabric.

We have a Dev workspace containing artifacts for ingesting data, storing data, cleaning & feature engineering, model training & logging experiments, and finally the best model is selected from the experiment and saved as a new version of the model.

The problem is ML models are the only artifacts that can't be directly deployed using the pipeline. I know it's possible to save the models to the Dev workspace files, then copy them to the test / prod workspace files, then re-register the models from the files in to the model registry. However this process contains a lot of manual steps which introduces more room for error.

I was hoping that someone could point to a successful use case that I can reference or give advice on the best way to structure the workspaces for ML Ops.

Thanks