In this presentation, we’ll provide practical guidance on overcoming key challenges in deploying AI at the edge, including remotely managing containerized models in resource-constrained environments using scalable, purpose-built infrastructure. We’ll also cover selecting and integrating the right hardware and software for high-performance edge vision systems that bridge the gap between edge inference and cloud management to enable seamless AI operations. In the demanding landscape of embedded vision and edge AI, maximizing performance and efficiency is paramount. We’ll explore how to leverage rugged industrial PCs with scalable container orchestration to avoid an over-resourced edge stack that overwhelms with manual operations and lacks operational overview. Learn how to build an edge AI stack that unlocks real-time, efficient and reliable vision-based solutions and discover how a best-of-breed approach enables rapid iteration and future-proofs your edge AI solutions.