Has anyone served a custom MLFlow model and that was trained and saved in kedro via

kedro-mlflow

? I have trained a custom mock model in which I define operations to save internal data sources to an mlflow run and load them into the desired custom

mlflow.pyfunc.PythonModel

. I am able to serve the model using

--no-conda

. Without this flag I get an error raised in which

ModuleNotFoundError: No module named '<kedro_package_name>'

. In my MLModel file I set

loader_module: <kedro_package_name>.extras.mlflow.loader_cosine_model

. I can also provide any other additional details if anyone can help!