Develop and Deploy Deep Learning Services at the Edge with IBM
IBM's edge solution enables developers to securely and autonomously deploy Deep Learning services on many Linux edge devices including GPU-enabled platforms such as the Jetson TX2. Leveraging JetPack 3.2's Docker support, developers can easily build, test, and deploy complex cognitive services with GPU access for vision and audio inference, analytics, and other deep learning services.
With IBM's edge solution beta, developers can register their custom service containers in IBM's Cloud repository, deploy and manage their services across multiple devices using Watson IoT Platform, collect insights from each edge, and analyze with IBM Watson and Cloud services.
Watch this webinar replay to learn how to:
- Register for IBM Watson IoT Platform services, including IBM's edge solution.
- Enable custom TX2 Deep Learning services and multi-service patterns, to be deployed over IBM's edge solution. (Demo: AI and Deep Learning inference on TX2, leveraging TensorFlow, OpenCV, Keras, TensorRT, Watson-Intu).
- Manage and collect insights from multiple Edge nodes using Watson IoT Platform, and display aggregate statistics.
ON DEMAND WEBINAR REGISTRATION
Presented By
Chris Dye
Senior Software Developer, IBM
Chris is a member of IBM Watson Cloud's Applied Sciences group, working on Deep Learning, Edge Compute, and Embodiment / Cognitive applications. Chris is an aerospace engineer, with a background in engineering methods, optimization, predictive analytics, and systems design for commercial and military aircraft engines, and industrial land and sea-based gas turbines. Hobbies include 3d printing, automotive tuning, and mountain biking.
Amit Goel
Product Line Manager of Autonomous Machines, NVIDIA
Amit Goel is a product line manager of autonomous machines at NVIDIA, where he leads the product development of NVIDIA Jetson, the most advanced platform for AI computing at the edge. Amit has more than 10 years of experience in the technology industry working in both software and hardware design roles. Prior to joining NVIDIA in 2011, he worked as a senior software engineer at Synopsys, where he developed algorithms for statistical performance modeling of digital designs. Amit holds a Bachelor of Engineering in electronics and communication from Delhi College of Engineering, a Master of Science in electrical engineering from Arizona State University, and an MBA from the University of California at Berkeley.
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