They need to add a couple minor painful paths that people usually use package managers with (private indexes, binding packages to third party indexes...) but if you don't need fancy corner cases, it's the best thing ever.
One thing I wonder is how many data scientists will use this feature given that it is not enabled by default (which is understandable, would be messy for every notebook to have a venv), and only via command-line arguments.
I guess this is easily remedied by helpful beginner tooltip UX ("This notebook has several dependencies. Would you like to build it in a sandbox?").
We would also like to auto-detect a pyproject.toml, and use that when desired.
I haven't tried this yet, but I love that the functionality of Jupytext is also incorporated, so I guess you get to reproduce the whole end product and all dependencies just from a plain-text script that you can track in Git.
