Every time I've convinced data scientists to just hand off an ONNX file to me for Production everyone comes out pleased: building an ONNX file is easier than productionizing a notebook any other way; the speed and performance of ONNX runtime are great and it is more easily integrated in C#-based pipelines (potentially avoiding expensive data transfers to/from Python VMs).
The biggest, ugliest hurdle I've seen is how many pre/post-processing steps data scientists tend to accidental convince themselves "can only be done in Python" either because they don't have time to research alternatives, believe Python to be inherently magical, or found some massive, obscure Huggingface-like corpus with gigabytes of data that would result in the most bloated ONNX files and "obscured in a Python or VM install step" hides how big the corpus is.