5 Things Anyone Doing Computer Vision At the Edge Should Know

The convergence of artificial intelligence and edge computing is transforming industries by enabling real-time decision-making and smarter operations. In particular, computer vision at the edge enables several use cases for fleet management, including predictive maintenance, object or defect detection, retail shrinkage avoidance, worker safety, and quality control. However, developing, deploying, monitoring, and orchestrating computer vision AI models at the edge introduces complex challenges—from limited computing resources, and dealing with a manual, time-intensive deployment process, to managing large-scale, distributed systems securely and efficiently.

In this exclusive joint webinar, ZEDEDA and Edge Impulse will explore how businesses can overcome these challenges and unlock the full potential of AI at the edge, leveraging advancements from both platforms. Join Aurelien Lequertier, Lead Solutions Architect at Edge Impulse, and Yogesh Soni, Principal Product Manager at ZEDEDA, as they explain key challenges that must be addressed to successfully implement computer vision at the edge.

You’ll learn:

• How and why data handling and contextualization at the edge matters
• How to manage diverse AI applications on a large fleet of heterogeneous devices
• Best practices for protecting data integrity and edge device integration
• And much more!