Complete, unified device management solution
Peridio provides planned, staged deployments and optimized delta updates for direct interaction with devices, plus device and authentication provisioning. Traditionally, updates were challenging - costly and time-intensive - but Peridio makes them straightforward.
Join us for an in-depth discussion with LeafLabs, whose engineers have developed embedded systems for environments ranging from deep sea to space, as they share practical insights from their work at the intersection of AI and embedded systems.
In this technical panel discussion, we'll explore the real-world challenges of implementing AI in resource-constrained environments, from selecting the right hardware architecture to managing firmware updates for AI models in the field. LeafLabs will share their experience balancing competing demands of processing power, battery life, and reliable operation, while addressing critical security considerations for AI-enabled devices.
Key Topics Include:
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Bridging the gap between AI development and embedded deployment
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Making informed architecture decisions: hardware selection, compute location (edge vs. cloud), and security implementation
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Questioning assumptions and understanding the "why" behind technical requirements
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Managing updates for AI models and firmware across device fleets
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Optimizing resources: balancing power consumption, memory management, and cost considerations
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Leveraging platforms like Peridio to decrease engineering costs and simplify maintenance
Whether you're working on your first AI-enabled product or scaling an existing fleet of devices, this session will provide valuable insights you won’t want to miss.
Panelists
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Bill Brock - CEO @ Peridio
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Justin Schneck - CPO @ Peridio
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Jeff Ciesielski - Director of Research @ LeafLabs
Who Should Attend:
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Firmware Development Teams
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DevOps Engineers
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Product Managers
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Quality Assurance Teams
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Technical Decision Makers
Join us to discover the strategies and tools that are helping leading organizations ship firmware updates with confidence and predictability.