I completely agree with the cons you outlined, especially your point about "productivity drops when data scientists leave notebooks."
A few years ago, I started working as a data scientist at a big financial firm and reviewed all workflow orchestrator available tools (including Kedro). I didn't like that all of them forced me to re-write my Jupyter code into their frameworks (they're supposed to make me more productive, not less).
True, notebooks have their issues but they can be fixed (I don't buy that "Jupyter is only for prototyping argument"). So, long story short, I started a project with a friend that makes us more productive by fixing the problems that notebooks' problems. https://github.com/ploomber/ploomber
This is a daily pain we've experienced while working in the industry! Our projects would usually allocate a few weeks to refactor notebooks before deployment! So we started working on an open-source framework to help us produce maintainable work from Jupyter. It allows easy git collaboration and eases deployment. https://github.com/ploomber/ploomber
Is there support for including custom code as part of the deployment?
Models trained on tabular data (which are the majority of use cases in industry) usually require some pre-processing before feeding the model (i.e., feature engineering).