Artificial Intelligence thrives on data—especially timely data. In this talk, we’ll explore how to integrate event-driven architectures with popular AI/ML frameworks to unlock real-time intelligence. We’ll dive into the nuts and bolts of constructing a continuous data pipeline using open-source technologies like Kafka Streams, Apache Flink, and popular AI libraries such as TensorFlow or PyTorch. We’ll walk through end-to-end examples: from data ingestion, cleaning, and feature extraction, to model inference in near-real time. You’ll discover how to optimize model performance under streaming conditions, employing sliding windows and advanced time-series techniques. Additionally, we’ll address operational challenges such as model updates in production, handling concept drift, and balancing compute resources with streaming throughput demands. Attendees will leave with a blueprint for setting up an event-driven AI pipeline, armed with concrete tips on choosing the right open-source frameworks, monitoring streaming model performance, and orchestrating seamless model deployments. If you’ve ever wondered how to blend AI with real-time event processing to deliver actionable insights the moment they matter, this session is for you.