Adaptive Recommendations at Scale Using AI

About this session

As recommendation systems evolve from batch-based personalization to adaptive, real-time discovery engines, infrastructure must transform too. This talk draws from large-scale experiences powering billions of recommendations and searches per day—spanning event-driven architectures, model-driven indexing, and feedback-loops built for continuous learning.We’ll explore how to evolve traditional recommender pipelines to support AI-native workloads—where training, inference, and evaluation all coexist in production. You’ll learn how leaders can architect not just systems, but trust and ambiguity—balancing experimentation and reliability while enabling teams to thrive amid shifting signals.This is a story about how distributed systems meet adaptive intelligence—and what it takes to make recommendations feel instant, relevant, and alive.In the era of AI-driven personalization, recommendation systems are no longer static ranking engines—they are adaptive ecosystems that continuously evolve through user feedback, contextual cues, and model retraining.This talk unpacks how large-scale distributed systems can transform to support this new paradigm of real-time discovery. Drawing from experience designing large recommendation and catalog-slicing infrastructures across consumer platforms, we’ll explore:Event-Driven Adaptation — How streaming signals and micro-batches bridge online and offline learning, turning user interactions into live feedback loops.Infra for Continuous Intelligence — Designing infra that allows training, inference, and evaluation to coexist seamlessly, without fragmenting data pipelines or reliability.Architecting for Ambiguity — Techniques for scaling before the problem is fully defined—building systems that embrace evolving data distributions and model behaviors.Trust & Leadership in the Loop — Why leaders must treat “infra debt” and “model debt” with equal rigor, architecting psychological and technical safety so teams can innovate responsibly.Dual-Path Execution — How maintaining separate product and infra strategies for AI adoption enables organizations to ship fast while investing deep.By the end, attendees will walk away with a playbook for evolving legacy recommender systems into adaptive, AI-native discovery platforms—and insights on how to lead teams confidently through that transition.Key TakeawaysHow to evolve recommendation infrastructure for real-time, adaptive intelligence.How to architect for ambiguity—designing systems and teams that thrive amid uncertainty.Why evaluation and feedback loops are as critical as training.How to balance product innovation with infra stability through dual execution paths.Leadership lessons for navigating the AI-driven evolution of large distributed systems.