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Thursday, April 11 • 10:15am - 10:45am
Auto-What? A Taxonomy of Automated Machine Learning

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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. 


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Speakers
avatar for Adam Blum

Adam Blum

CEO, Deep Learn
CEO/CTO of several technology startups, adjunct professor at CMU and UC Berkeley, author of Neural Networks in C++, several patents in machine learning and related disciplines

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