Could that be because of using Jupyter notebook itself? I like Jupyter for data and machine learning 'journalism', but I don't see it as the a proper medium to address the 'last-mile'. The insights driven from Jupyter, in my opinion, are not actionable and well integrated enough. It is becoming a de-facto medium reminding me of shared Excel files.
Could be. Using Jupyter for ML development or even prototyping (as opposed to presentations / demonstration / teaching like the OP — that's where Jupyter really shines) is a red flag.
I see a similar pattern with Pandas: some people use Pandas not because it's the right tool for the job (Pandas has many strengths), but because they're scared of writing comprehension loops and basic data structures. To avoid the CS-y stuff. But without the CS-y stuff, the result ends up a mess of lambdas, weird reindexing and buggy copy/view semantics.
And then "the next guy", the one who's job it is to clean up and productionalize the maverick's output, ends up having to reinvent and fix the entire solution. Basically doing both jobs.
Yes, Jupyter is for initial exploration. Then you write solid normal production code. Then you might write further notebooks that import that production code and run/visualize metrics and reporting for your client (probably non-technical people).
I had a "data scientist" submit notebooks to us as if we could ship any of that in production. (We fired him.) It's for hacking and blogging, not for production work.
