CapyMOA: Stream Learning for Evolving Data

Machine learning is no longer confined to static datasets. Many modern problems — from energy forecasting and fraud detection to sensor analytics and cybersecurity — involve continuous streams of data. These streams arrive instance by instance, evolve over time, and often undergo concept drift, making traditional batch learning unsuitable. This post is for ML practitioners and applied researchers working with evolving data streams who need high-throughput, adaptive learning – and want MOA-level.