My main question is how/if DeepNote addresses issues of reproducibility. Is this a priority for your team? You mention it a few times in your post here, but there is not so much in the docs -- I looked it up in and got just to this:
> Even though the Custom environment cache is implemented using Docker images, it doesn't primarily serve the reproducibility problem. The aim of the feature is to significantly speed up the start time of your projects. In other words, you should consider it to be only a cache at this point.
My experience with Notebooks suggests that the main (computational) reproducibility challenges were
A) 'hidden state' information (e.g. cells executed out of order, variables changed and then reverted but not re-run); and
B) no clear infrastructure for documenting/caching dependencies (I see you have a terminal option, and the web-based access should address some of this, but something like 'conda install environment.yml` doesn't seem possible out of the box.)
I would understand if these issues are not priorities for you, I don't think most data science projects need to be run in the far future and most teams can informally sync their dependencies.
If reproducibility is a core priority, do you plan to write something about DN serves that purpose? I'd be glad to take a close look if you do (I have written/worked a fair bit on this in the past).