Imagine wanting to combine computer vision, object detection, and other sensors to enable real-time AI tracking and measurement of events at a certain location—a shopping mall, a studio, a light manufacturing floor, a clinic. Designing such a system, you would quickly realize that the real-time capability would not be possible with conventional, cloud- or data-center-based AI. Latency to the hosting cloud or data center is too high and the bandwidth too low. You have discovered one of the many scenarios requiring AI at the edge.

But AI at the edge is a different animal from conventional cloud- and data-center-based AI. What kind of AI development do you need to process massive volumes of data and ensure accurate results or make informed decisions in real time?

We will demonstrate how shifting AI processing from the cloud to the edge can dramatically reduce costs and improve performance; delve into FSP’s cloud and edge inference testing to understand the crucial differences between cloud and edge AI; and show the unique advantages of processing data closer to the source.

We’ll also cover how to navigate the challenges of edge AI implementations, including connectivity, resource constraints, and the need for greater control over AI solutions. Finally, we'll delve into the elements of selecting the right tools and platforms for edge AI development and deployment so you can effectively scale solutions from a single implementation to many.

Who Should Attend:

• AI/ML Professionals: Expand your knowledge of edge AI.
• Software Developers: Learn to implement and deploy AI at the edge.
• IT/Infrastructure Managers: Manage IT infrastructure and edge deployments.
• Business Leaders and Decision-Makers: Explore edge AI for your business.
• Companies without AI teams: Use AI without needing AI engineers.
• Anyone interested in AI: Stay ahead of the curve with edge AI insights.