AutoML is one of the most robust areas of innovation in applied machine learning. New products in this space from the likes of Google and new AI-focused startups are appearing constantly, all of which promise to make machine learning accessible to the masses without the need for trained data scientists. At its base, AutoML involves some selection and configuration of machine learning algorithms. However, each product seems to have its own take of what parts of the machine learning process to automate and how they do it.
Based on questions I have seen, I believe the industry could use a taxonomy of capabilities of AutoML tools. These capabilities include the following: choosing algorithms, setting hyperparameters, controlling model search and training time, cross-validation, data preprocessing, and feature creation. While Gartner has yet to offer a Magic Quadrant for AutoML, perhaps this overview can help inform a future effort as the automated machine learning sector matures.