Modern AI systems increasingly require real-time decision making, low-latency data access, and the ability to scale across distributed environments. In-memory computing platforms such as Apache Ignite provide a powerful foundation for building intelligent, high-volume applications that demand speed and resilience.

This session explores how distributed data grids can power next-generation AI systems by enabling real-time feature access, low-latency model inference, and scalable data pipelines. The talk will discuss architectural patterns for combining Apache Ignite with cloud-native platforms, AI pipelines, and event-driven systems.

Attendees will gain insights into building resilient data architectures that support large-scale AI workloads while maintaining performance, scalability, and operational reliability.