Mar 15–18, 2020

Schedule: AI at the Edge sessions

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11:00am11:40am Tuesday, March 17, 2020
Location: 210 B
Jameson Toole (Fritz AI)
Getting machine learning (ML) models ready for use on device is a major challenge. Jameson Toole explains optimization, pruning, and compression techniques that keep app sizes small and inference speeds high. You'll learn to apply these techniques using mobile ML frameworks such as Core ML and TensorFlow Lite. Read more.
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11:00am12:30pm Tuesday, March 17, 2020
Location: 210 D/H
Joseph Nelson (Roboflow)
Computer vision gives you the ability to make anything in the real world into read/write on your phone. Joseph Nelson walks you through the end-to-end flow required to train a model for mobile deployment, including image collection, preprocessing and augmenting considerations, model training, and saving the TensorFlow Lite (TFLite) model in an appropriate format for deployment. Read more.
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11:50am12:30pm Tuesday, March 17, 2020
Location: 210 B
Sukanya Mandal (Capgemini)
Heavy ML computation on resource-constrained IoT devices is a challenge. IoT demands near-zero latency, high bandwidth availability, continuous and seamless availability, and privacy. The right infrastructure derives the right ROI. This is where edge and cloud comes in. Sukanya Mandal explains how training ML models at the cloud and inferencing at the edge has made many IoT use cases plausible. Read more.
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2:35pm3:15pm Tuesday, March 17, 2020
Location: 210 B
Donagh Horgan (Extreme Networks)
Machines talk among themselves, but you may understand their behavior by analyzing their language. Donagh Horgan outlines a lightweight approach for securing large internet of things (IoT) deployments by leveraging modern natural language processing (NLP) techniques. Rather than attempting cumbersome firewall rules, IoT deployments can be efficiently secured by online behavioral modeling. Read more.

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