But these do exist: plain Jax or Pytorch only give you basic linear algebra, differentiation and some basic layers. And there's a plethora of more or less advanced libraries that add specific functionality, for example torch geometric for graph data and lightning to reduce boilerplate.
Chiming in to say that this precisely our observation. The existing ML/DL libraries are not bad as far as those types of things go. In fact, Pytorch is an amazing library IMO. Especially compared to TensorFlow, Caffe and the stuff that came before that.
But like George points out in the article, unlike "traditional" software, ML requires iteration, data management, monitoring, specific infra reqs, and so on. So our take was that libraries would never be enough, hence the SaaS offering.