Machine learning is still too hard to use

The reality is that for many of applied machine learning's use cases, there is no need to train a new model from scratch. For example, if you are developing a conversational agent, Google's Meena is almost certainly going to outperform your model. If you're developing a text generator, you should use OpenAI's GPT-2 instead of building your own from scratch. For object detection, a model like YOLOv3 is probably your best bet. Thanks to transfer learning--a process in which the "knowledge" of a neural network is fine tuned to a new domain--you can take a relatively small amount of data and fine tune these open source, state-of-the-art models to your task.