Streaming AI/ML with Apache Kafka: Real-Time Patterns for Modern Intelligence

AI and Machine Learning systems only create value through the data that feeds them. As environments change continuously, static datasets and offline pipelines are no longer sufficient. Modern AI/ML requires real-time data streams to remain accurate, responsive, and trustworthy.

This session explores how Apache Kafka acts as the central event backbone for AI/ML, connecting data producers, feature pipelines, training processes, inference services, and feedback loops. Kafka enables event-driven AI/ML systems that evolve alongside the data they observe.

The talk introduces a set of AI/ML streaming patterns that show how Machine Learning workloads can be built around continuous ingestion rather than periodic batch jobs. It covers patterns for streaming data ingestion and preprocessing, live features extraction, and training models directly from event streams, including near-real-time retraining and online learning approaches. It also explores real-time inference and feedback-loop patterns, where predictions and outcomes are streamed back into Kafka for monitoring and model drift detection, as well as LLM-oriented patterns where Kafka provides continuously refreshed context for Retrieval-Augmented Generation (RAG) architectures. By focusing on architectural patterns rather than isolated tools, the session highlights how data timeliness, ordering, and flow directly shape the effectiveness of Machine Learning systems.

Attendees will leave with a practical pattern-based toolkit for building real-time AI/ML systems: designing feature pipelines, implementing online learning, integrating feedback loops, and supporting advanced applications such as LLM-driven RAG workflows.