As AI migrates out of the data center to meet the growing demand for real-time AI at the Edge, developers face entirely different AI compute requirements, a broad range of application development challenges & deployment realities, and a more complex heterogeneous infrastructure. AI theory meets the challenges of practical, day-to-day AI applications at the Edge, where technology limitations can force product compromise on developers.
November 5, 2018
Date: Nov 12, 2020
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As AI migrates out of the data center to meet the growing demand for real-time AI at the Edge, developers face entirely different AI compute requirements, a broad range of application development challenges & deployment realities, and a more complex heterogeneous infrastructure. AI theory meets the challenges of practical, day-to-day AI applications at the Edge, where technology limitations can force product compromise on developers.
In this webinar, we explore the challenges, gaps and opportunities for AI to be integrated into a diverse range of edge products and applications improving and creating new industrial systems.
Key topics include:
The AI deployment spectrum from Enterprise to Edge
AI Edge state of market, gaps and opportunities
Why the Edge demands a different approach
The balancing act of performance/power budget/cost/complexity
Making Edge AI practical in widely varying resource situations
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