Learning on the Flow – Stream Mining in AI Systems

Modern machine learning systems increasingly operate on continuous data streams that evolve over time. Without methods that can adapt to concept drift and resource constraints, models quickly become unreliable in real-world settings. In our four-part Stream Mining series, Lamarr researchers explore foundations, efficient ensemble methods, streaming gradient boosting, and practical tools such as CapyMOA, highlighting how online learning enables robust, adaptive, and production-ready AI systems.