Hi @SOlivero
Make sure that the model was in fact saved with the provided URI.
The latest keyword will retrieve the latest version of the registered model when mlflow.pyfunc.load_model('models:/model_name/latest') is executed, not the highest version. If you have not registered the model with the name model_name in the models registry you can try to load it using the full path:
mlflow.pyfunc.load_model("file:///path/to/the/saved/model")
where /path/to/the/saved/model represents the location where the model artifacts were saved during training (i.e the path where mlflow.pyfunc.save_model() was called during training).
If this doesn't help, check that you can access the model outside of the notebook. You could also check that the Databricks cluster you're running on has access to the Databricks models registry if that is where your model was saved.
