The point of autoML from what I've gathered is to make it as easy as can be to get a model working in production. AutoML at least AutoML Vision is using transfer learning to retrain X number of layers from their algorithm (the one google uses is escaping me right now). The number of layers it has to retrain is the value they offer, it tries and tries optimizing for accuracy.
I've had good results with it, but you do have to do things their way and its not always well documented. If you want more control you should create your own model and host it on google app engine, otherwise AutoML is what it is, no way to customize or tune it other than changing the training data you give it.
I've had good results with it, but you do have to do things their way and its not always well documented. If you want more control you should create your own model and host it on google app engine, otherwise AutoML is what it is, no way to customize or tune it other than changing the training data you give it.