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Retail AI is rapidly shifting from experimentation to full-scale deployment, creating new infrastructure requirements that span store-edge environments, regional processing hubs, and centralized AI data centers. Real-time analytics, recommendation systems, and supply chain optimization demand scalable, energy-efficient architectures capable of delivering consistent performance from edge to core. This showcase explores how to architect AI-ready infrastructure that balances compute density, operational efficiency, and sustainability while enabling high-performance, cost-effective AI transformation across distributed retail environments. Join this episode to discover:

• How AI workloads are reshaping infrastructure requirements across edge, regional, and core data center environments
• Key design considerations for balancing performance, scalability, and energy efficiency in retail AI deployments
• Practical strategies for integrating real-time analytics and AI inference from store edge to centralized AI facilities
• Architectural approaches to support sustainable growth while reducing operational cost and complexity