Jayakumar Ramalingam

Event-Driven Intelligence: Architecting Real-Time AI Decision Systems at Scale

Abstract:

Event-driven intelligence is emerging as a foundation for AI systems that interpret high-volume data streams, adapt to evolving context, and make reliable real-time decisions. This keynote presents a unified approach to architecting scalable intelligent platforms using event streaming, stateful processing, cloud-native microservices, real-time feature computation, and machine learning model serving. It examines challenges across data, runtime, and system layers, including event ordering, schema evolution, backpressure, state consistency, concept drift, inference latency, observability, fault recovery, governance, and cost. Drawing on production experience with high-traffic personalization and recommendation platforms, it introduces a six-dimensional evaluation framework spanning latency, throughput, decision quality, adaptability, resilience, and operational efficiency. The talk concludes with emerging directions in online learning, responsible personalization, agentic decision systems, continuous intelligence, and self-healing architectures.

Profile:

Jayakumar Ramalingam is a Staff Software Engineer at SiriusXM with 16+ years of experience architecting cloud-native distributed systems, event-driven platforms, and real-time data services. He contributes to high-traffic personalization and recommendation systems supporting SiriusXM and Pandora digital experiences. His professional experience spans the media, retail, automotive, and financial services industries. A Senior Member of IEEE, his current research focuses on event-driven intelligence, real-time AI decision systems, scalable recommendation architectures, and autonomous self-healing cloud platforms.