The introduction of low-cost, high-performance embedded processors coupled with improvements in Neural Network model optimization lay the foundation for AI and Computer Vision at the edge. Moving intelligence from the cloud to the edge offers many advantages including the reduction of network traffic, predicable ML inference times, and data security to name a few. Challenges exist as many development teams do not have data scientist or AI development engineers. What is needed are practical AI solutions including ML development tools, optimized inference engines and reference platforms that will abstract out the development complexities to stream line prototyping and development.
In this joint webinar with Au-Zone Technologies we will discuss:
- Development challenges and solutions which can be use to enable AI/ML at the edge to implement object detection, classification and tracking for medical and industrial use-cases
- Visualization techniques for activity monitoring and object detection