As a scientist designing risk management systems, I also like to:

. avoid moving the data;

. bring the (ML/stats) code to the data;

. make in-memory computations (when possible) to reduce latency (network+disk);

. work on live data instead of copies that drift out-of-date; and

. write software to keep models up to date because they drift with time too and that's a major, operationally un-noticed, and extremely costly problem.

I'm not yet into Tensor/ML-Flow, but I use R, JS, and Postgres, thereby relying on open-source eco-systems (and packages) that are:

. as standard as possible;

. well-maintained;

. with a long expected support; and

. as few dependencies as possible.