1. At what sort of scale does Metaflow become useful? Would you expect Metaflow to augment the productivity of a lone data scientist working by himself? Or is it more likely that you would need 3, 10, 25, or more data scientists before Metaflow is likely to become useful?
2. When you move to a new text editor, there are some initial frictions while you're trying to wrap your head around how things work. So, it can take some time before you become productive. Analogously, I imagine there are initial frictions when moving to Metaflow. In your experience, after Metaflow's environment has already been established, how long does it take for data scientists to get back to their initial productivity? It would be useful to have a sense of this for the data scientist who would want to sell their organization on adopting Metaflow.
3. Many data scientists work in organizations which have far less mature data infrastructure than Netflix, and/or data science needs of a much smaller scale than Netflix. In particular, I may not even have batch processing needs (e.g. a social scientist working on datasets which can be held entirely in memory). In that case, is Metaflow useful?
4. What's the closest open-source alternative to Metaflow on the market? Off the top of my head, I can't think of anything which quite matches.