Real-time AI systems depend on more than model quality. In production, the value of an AI decision often depends on whether the system can capture fresh user signals, transform them into reliable features, serve them within latency limits, and recover gracefully when pipelines or models degrade.

This session presents a practical architecture for real-time AI feature pipelines, using personalization and recommendation systems as the guiding example. We will examine how clickstream events, streaming ingestion, feature derivation, online feature stores, model inference, ranking services, caching, observability, experimentation, and fallback behavior work together to support reliable AI decisions in production. The talk draws from production experience operating these systems at streaming media scale, including concrete failure modes, instrumentation patterns, and operational tradeoffs encountered when serving millions of users.

Attendees will learn how to design AI systems that balance freshness, latency, accuracy, resilience, and business value. The session is especially useful for AI engineers, ML platform teams, data engineers, backend engineers, and architects moving AI applications from prototype to production.