Fun read on why MLOps is still somewhat broken -- the engineers who build them are not users.
In ML Frameworks, the authors were ML scientists -- (Py)Torch, Theano, Caffe, MXNet, Keras, Chainer, TF, etc. and that helped in design requirements accurately being in your head.
Bananas and ML infrastructure: I've asked around about cloud workflows, and most of the feedback had unhappiness with cloud tooling. This prompted a discussion in
@chipro's MLops community -- why are MLops frameworks so bad? (1/9)