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 a principal data scientist lead in AzureCAT within the Cloud AI Platform Division at Microsoft, where she leads an international team of data scientists and engineers to build predictive analytics and machine learning solutions with external companies utilizing Microsoft’s Cloud AI platform. Previously, she was a data scientist at Nokia, where she produced business value and insights from big data through data mining and statistical modeling on data-driven projects that impacted a range of businesses, products, and initiatives. Danielle holds a PhD in quantitative psychology from the University of North Carolina at Chapel Hill, where she studied the application of multilevel event history models to understand the timing and processes leading to events between dyads within social networks.
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