Power of vision AI at the edge: How to unlock simple, fast edge AI deployment
Whether implementing worker safety measures, monitoring people in manufacturing plants or developing newer AI-powered applications, more design engineers are shifting their focus to inference at the Edge.
And for good reason. In response to consumer and industrial demand, chipmakers and equipment manufacturers alike are racing to strengthen vision edge AI offerings while collaborating with partners across the design chain.
That’s certainly the case with Avnet’s /IOTCONNECT™ solution, which provides the infrastructure needed to move quickly from concept to deployment while maintaining security and reliability.
Avnet’s /IOTCONNECT speeds Edge AI implementations by bridging the gaps between hardware capabilities to cloud infrastructure and, finally, to the device.
The secret sauce to edge AI: Pre-built integrations
Pre-built integrations eliminate stumbling blocks of edge AI development. engineers can skip weeks of configuration and jump straight into building their core applications. The platform's web dashboard serves as mission control, letting developers deploy AI models, manage remote devices and monitor solutions in real time.
Built on Amazon Web Services (AWS), /IOTCONNECT provides a unified interface that simplifies complex integration challenges typically associated with Edge AI deployments. The platform has demonstrated its versatility through successful integrations with multiple leading computing solutions.
For developers working with Edge AI applications, /IOTCONNECT eliminates many common implementation hurdles. The platform offers pre-configured integration with various development boards, enabling engineers to focus on their core applications rather than spending time on underlying AWS service configurations. Through its web-based dashboard interface, /IOTCONNECT enables rapid deployment of AI models, remote device management and real-time monitoring of Edge AI solutions.
The platform's capabilities extend beyond basic connectivity, offering sophisticated features such as automated alerts, over-the-air updates and customizable data visualization tools. These features have proven valuable across diverse applications, from industrial equipment monitoring to smart city implementations.
Want to see vision Edge AI deployment in action? We want to show you how to use Tria’s RZBoard V2L for vision applications in everything from pet monitoring to parking occupancy detection. See how we create vision-based edge AI solutions with the STM32MP257 and how to rapidly create AI/ML edge solutions using NXP's new FRDM-IMX93 development board.