14–17 Oct 2019
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Technical Director, Machine Learning, iRobot
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.
9:00–12:30 Tuesday, 15 October 2019
Location: Windsor Suite
Secondary topics: Computer Vision, Machine Learning, Machine Learning tools
Danielle Dean (iRobot), Mathew Salvaris (Microsoft), Wee Hyong Tok (Microsoft)
(4.33, 6 ratings)
Danielle Dean, Mathew Salvaris, and Wee Hyong Tok outline the recommended ways to train and deploy Python models on Azure, ranging from running massively parallel hyperparameter tuning using Hyperdrive to deploying deep learning models on Kubernetes. Read more.
14:35–15:15 Wednesday, 16 October 2019
Location: King's Suite - Sandringham
Secondary topics: Machine Learning, Machine Learning tools
Danielle Dean (iRobot), Wee Hyong Tok (Microsoft), Mathew Salvaris (Microsoft)
(4.00, 2 ratings)
Dive into the the newly released GitHub repository for recommended ways to train and deploy models on Azure with Danielle Dean, Wee Hyong Tok, and Mathew Salvaris. The repository ranges from running massively parallel hyperparameter tuning using Hyperdrive to deploying deep learning models on Kubernetes. Read more.
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