The tedious but necessary process of selecting, testing, and tweaking machine learning models that power many of today’s AI systems is too time-consuming. Automated ML is at the forefront of Microsoft’s push to make Azure ML an end-to-end solution for anyone who wants to build and train models that make predictions from data and then deploy them anywhere—in the cloud, on-premises, or at the edge.
Join Danielle Dean for a surprising conversation about a data scientist’s dilemma, a researcher’s ingenuity, and how cloud, data, and AI came together to help build automated ML.
Danielle Dean is the technical director of machine learning at iRobot. Previously, she was a principal data science lead at Microsoft. She holds a PhD in quantitative psychology from the University of North Carolina at Chapel Hill.
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