Are you looking for advanced models that you can train on your data pretty much out-of-the-box or simple, easy-to-read models that help you learn the underlying concepts?
If it's the first case, what you want to do is find the best GitHub repos for the task(s) you are trying to do. Make sure the GitHub repo has a model zoo and good support and start from there. In CV, if you are trying to do high-end work, the repos to check out are:
If you want easy-to-read code for non-trivial tasks, I would suggest taking a look at Gluon (GluonCV - https://gluon-cv.mxnet.io/ and GluonNLP - https://gluon-nlp.mxnet.io/). I haven't worked much with the fast.ai library, but that's probably also a good suggestion.
I think this is what TF's Estimators are about. The idea is that they're going to reduce boilerplate on the assumption that most users of Tensorflow actually only use the same handful of models (ResNet, VGG, Inception, etc).
I've been learning Tensorflow recently for a side project, and the style transfer work I'm doing means I need to build my own Tensorflow graphs, so I haven't had much use for this kind of thing. But it sounds like it was made for you, not me.
Like there is a mostly finite set of typical solved things you would want to use ML for like image classification, object detection, etc.
I find myself spending my time copying an example verbatim and replacing the csv / images with my own.