Unlocking the Power of Streaming Data: Online Learning for Diverse Applications

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The book explores the challenges of processing data streams, which consist of large, potentially infinite sequences of rapidly arriving data from sources like sensor networks and customer interactions. It highlights the inadequacy of traditional static machine learning algorithms for these dynamic environments, emphasizing the need for techniques that can adapt to evolving data distributions, known as concept drifts. The focus is on maintaining performance while managing memory and processing constraints in the face of continuous data flow.