Schedule: Edge computing and Hardware sessions

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.

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.

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.

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.

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.