Danielle Dean
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.

Sessions

9:00–12:30 Tuesday, 15 October 2019

Location: Windsor Suite

Training and deploying Python models on Azure

Implementing AI

Secondary topics: Computer Vision, Machine Learning, Machine Learning tools

Danielle Dean (iRobot), Mathew Salvaris (Microsoft), Wee Hyong Tok (Microsoft)

Average rating:

(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

Azure AI reference architectures

Implementing AI

Secondary topics: Machine Learning, Machine Learning tools

Danielle Dean (iRobot), Wee Hyong Tok (Microsoft), Mathew Salvaris (Microsoft)

Average rating:

(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.