hi Dan, about the TF advantages, mostly the simple/obvious things:

Well integrated with colab/notebook.
Easy to train various different types of models all in one platform (and using the same evaluation), then you can just choose the best.
Easy to leverage pre-trained models for text/image/object detection/etc on the input (see tensorflow.org/hub 1) – one can use the output of these pre-trained model as the input of TF-DF models.

When it comes to productionization, it also work with some (but not all yet) tools of TFX: see tensorflow.org/tfx – things like data validation.
If one is dedicated, one can always stitch these things without using TF-DF, it just makes it easier.
Btw, about data confidentiality: colab.research.google.com 3 also works with your own (locally, or remotely) running kernel – so you can be sure to preserve confidentiality of data. I often do that when developing with colab – I can reload python files I’m editing locally. Same with jupyther notebook front-end, I just like colab’s interface better.
cheers,
Jan