14–17 Oct 2019
Mathew Salvaris

Mathew Salvaris
Data Scientist, Microsoft

Mathew Salvaris is a data scientist at Microsoft. Previously, Mathew was a data scientist for a small startup that provided analytics for fund managers; a postdoctoral researcher at UCL’s Institute of Cognitive Neuroscience, where he worked with Patrick Haggard in the area of volition and free will, devising models to decode human decisions in real time from the motor cortex using electroencephalography (EEG); and a postdoc in the University of Essex’s Brain Computer Interface Group, where he worked on BCIs for computer mouse control. Mathew holds a PhD in brain-computer interfaces and an MSc in distributed artificial intelligence.

Sessions

9:0012:30 Tuesday, 15 October 2019
Location: Windsor Suite
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.
11:0511:45 Wednesday, 16 October 2019
Location: King's Suite - Sandringham
Yan Zhang (Microsoft), Mathew Salvaris (Microsoft)
Average rating: ***..
(3.67, 3 ratings)
When IoT meets AI, a new round of innovations begins. Yan Zhang and Mathew Salvaris examine the methodology, practice, and tools around deploying machine learning models on the edge. They offer a step-by-step guide to creating an ML model using Python, packaging it in a Docker container, and deploying it as a local service on an edge device as well as deployment on GPU-enabled edge devices. Read more.
14:3515:15 Wednesday, 16 October 2019
Location: King's Suite - Sandringham
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.

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