About This Architecture

AI-powered shelf stock monitoring system using computer vision and real-time alerts across multi-AZ AWS infrastructure. Camera feeds flow through Python-based AI modules with object detection models, then to Spring Boot backends via API Gateway, triggering alerts and updating a primary-standby database pair with caching. High-availability design spans two availability zones with replicated AI and application tiers, fronted by ALB, WAF, and CDN serving an Angular dashboard to operators. This architecture demonstrates best practices for fault tolerance, horizontal scaling, and separation of concerns in production retail systems. Fork and customize this diagram on Diagrams.so to adapt subnets, add monitoring, or integrate with your inventory management platform.

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How do you design a highly available AWS architecture for real-time inventory monitoring using AI image analysis?

This diagram shows a production-grade multi-AZ AWS setup where camera feeds enter Python AI modules in public subnets, object detection models trigger API Gateway calls to replicated Spring Boot backends in private subnets, which update a primary database and standby replica with caching. ALB, WAF, and CDN protect the Angular operator dashboard, ensuring fault tolerance and horizontal scaling acro