I think that by definition this is a tradeoff. Most times you talk to data scientists that want a fully automated end-2-end solution, that doesn't require they change anything about their current workflow, and that any future modifications to their workflow would be supported as well.
That is magical thinking. I prefer best of breed solutions that integrate nicely with other best of breed solutions every day. That way if a tool doesn't suit you tomorrow, you can relatively easily swap it out for something better
It seems that the "all-in" platforms are too "rigid", and all of the point solutions for the things you mentioned aren't proven enough.