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AI at the Edge is a different operating environment

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00:46:59
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ABOUT THIS EPISODE

AI at the Edge is a different operating environment

What does “AI at the edge” really mean in 2026, and why does it matter now more than ever before? In this episode, we’re joined by Brandon Shibley, Edge AI Solutions Engineering Lead at Qualcomm’s Edge Impulse, to discuss the current state and future of Edge AI in 2026. We discuss Gen AI, Small Models, and Cascades of Models, along with real-world constraints like latency, power, and privacy. We also dive into the role of MLOps, evolving hardware, and how developers can start building practical edge AI systems today.

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IN THIS EPISODE

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SHOW NOTES 🔗

00:00:00Welcome to Practical AI
00:00:41Introducing the guest
00:01:39The State of Edge AI in 2026
00:08:30Characteristics of Edge Environments
00:12:21Understanding Physical AI vs Edge AI
00:14:21Latency and Real-Time Performance
00:17:26Cascading Models at the Edge
00:19:14Detecting and Analyzing Objects
00:20:12Advancements in Edge AI Tooling
00:21:34The Role of MLOps in Edge AI
00:24:22Understanding Agency in Edge AI
00:26:09Governance and Control in Distributed Systems
00:28:39The Rise of Smaller AI Models
00:29:36Edge Impulse's Unique Approach
00:35:58Efficiency and Power in Edge Computing
00:39:01Getting Started with Edge AI Projects
00:42:45Future Possibilities in Edge AI
00:46:18Outro

TRANSCRIPT 🔗

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