Experiments using notebooks are fine as long as they are well documented.
Having your services and library code as .py files you can import in is great.
The issue comes with how to move from experimentation to deployment. If you already have services/library code as .py files you make your life a lot easier. The issue comes when everything is spread across multiple, poorly documented notebooks. If you're working with an MLOps team it makes their life a nightmare to take those notebooks and conform them into something usable.
Jupyter is great when it is used in the right way.
Having your services and library code as .py files you can import in is great.
The issue comes with how to move from experimentation to deployment. If you already have services/library code as .py files you make your life a lot easier. The issue comes when everything is spread across multiple, poorly documented notebooks. If you're working with an MLOps team it makes their life a nightmare to take those notebooks and conform them into something usable.
Jupyter is great when it is used in the right way.