Delivering AI-driven decisions during major live events requires more than a traditional data lake or offline ML stack. It requires a real-time data infrastructure that can ingest, validate, govern, and serve signals fast enough to support personalization and decisioning under extreme load. In this session, Manoj Yerrasani will discuss the architectural shift from batch-oriented data platforms to low-latency streaming pipelines, feature stores, and real-time serving layers built for global streaming applications. Drawing on experience building platforms that support approximately 50 million subscribers across multiple streaming services, the talk will examine how to design resilient data systems that sustain tens of millions of concurrent viewers during high-stakes events such as the Olympics, NFL, and NBA. Attendees will learn practical approaches to reducing data latency, improving reliability, scaling real-time feature delivery, and protecting customer experience during audience spikes while enabling AI and ML systems to make timely decisions when platform performance matters most.