Put AI to Work
April 15-18, 2019
New York, NY

Schedule: Edge computing and Hardware sessions

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11:05am11:45am Wednesday, April 17, 2019
Implementing AI
Location: Trianon Ballroom
Mathew Salvaris (Microsoft), Fidan Boylu Uz (Microsoft)
Interested in deep learning models and how to deploy them on Kubernetes at production scale? Not sure if you need to use GPUs or CPUs? Mathew Salvaris and Fidan Boylu Uz help you out by providing a step-by-step guide to creating a pretrained deep learning model, packaging it in a Docker container, and deploying as a web service on a Kubernetes cluster. Read more.
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1:00pm1:40pm Wednesday, April 17, 2019
Implementing AI
Location: Trianon Ballroom
JIAN CHANG (Alibaba Group), Sanjian Chen (Alibaba Group)
Average rating: *****
(5.00, 5 ratings)
Jian Chang and Sanjian Chen outline the design of the AI engine built on Alibaba’s TSDB service, which enables fast and complex analytics of large-scale time series data in many business domains. Join in to see how TSDB empowers companies across various industries to better understand data trends, discover anomalies, manage risks, and boost efficiency. Read more.
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1:50pm2:30pm Wednesday, April 17, 2019
Implementing AI
Location: Regent Parlor
Simon Crosby (SWIM.AI)
Average rating: ****.
(4.00, 1 rating)
Today’s approach to processing streaming data is based on legacy big-data centric architectures, the cloud, and the assumption that organizations have access to data scientists to make sense of it all—leaving organizations increasingly overwhelmed. Simon Crosby shares a new architecture for edge intelligence that turns this thinking on its head. Read more.
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4:55pm5:35pm Wednesday, April 17, 2019
Implementing AI
Location: Trianon Ballroom
Ted Way (Microsoft), Maharshi Patel (Microsoft), Aishani Bhalla (Microsoft)
Deep neural networks (DNNs) have enabled AI breakthroughs, but serving DNNs at scale has been challenging: Fast and cheap? Won’t be accurate. Fast and accurate? Won’t be cheap. Join Ted Way, Maharshi Patel, and Aishani Bhalla to learn how to use Python and TensorFlow to train and deploy computer vision models on Intel FPGAs with Azure Machine Learning and Project Brainwave. Read more.
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4:55pm5:35pm Thursday, April 18, 2019
Jennifer Fernick (NCC Group )
Quantum computers will enable us to efficiently compute things never thought possible, but how will this impact artificial intelligence? Jennifer Fernick explains how to filter signal from noise in discussions surrounding quantum machine learning by exploring how quantum computers work, what types of AI problems they may be good at, and which industries and use cases will (and won't) benefit. Read more.