I agree on the premise: yes eventually every analysis needs to become an app. Also let me add: no, dashboards are not going to cut it, they don't offer enough interactivity. I also love that the app here is a script and hence can be version controlled with git. However, there is no description anywhere of what happens when you need to scale with this. If you have to go from a couple of testers to 100 internal users like it very often happens in analytics, how does this react?
Also caching is a great idea but I would expect a lot of this logic to be managed on the server side, or I am missing something and ML is different here? I would expect to pipe as little data as possible back to the application because I want the user to wait max 3-4 seconds for the app to load at start.
As far as I can tell the caching is happening server-side. Pretty much all the frontend seems to be doing is poking the back-end to re-run the script (or get results from cache) and then getting back the diff to apply to the UI.
Also caching is a great idea but I would expect a lot of this logic to be managed on the server side, or I am missing something and ML is different here? I would expect to pipe as little data as possible back to the application because I want the user to wait max 3-4 seconds for the app to load at start.