Auto What? - A Taxonomy of Automated Machine Learning

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. We 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. This talk will cover each of these topic and discuss tools and techniques available in each area. Questions Answered For Audience: How can automated machine learning tools help me solve my machine learning problem faster and better? What are some tools available in each of these areas that can make me more productive as a machine learning practitioner? What new areas of my overall machine learning pipeline could be amenable to some form of automation?