Session overview
Edge AI is not a new phenomenon. This session will show how models for video, audio, and sensor data run on devices close to where data is created. The result is faster response, better privacy, and steadier cost. The cloud still matters for training and fleet insight, but the moment-to-moment work happens on the device. We’ll walk a full path from idea to production. Start with a model that fits your hardware. Trim it, test it, then measure real workloads. Deploy to what you already own or to dedicated edge gear. You leave with a playbook that you can repeat. Pick, prepare, test, ship, observe, improve. No lock-in. No mystery. Just a clear way to move intelligence out of the data center and into the real world.
Who this is for
This session is for engineers and architects evaluating local inference for video, audio, sensor, and other latency- or privacy-sensitive workloads.
What attendees learn
- How to choose, prepare, and test a model for the hardware where it will run.
- How to measure real workloads before deploying an edge model.
- A repeatable path from model selection through deployment, observation, and improvement.
Presentation history
Presentation history will be added as verified appearances are documented.
Book this session
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