Stream processing enables real-time analysis of continuous data streams, powering applications like fraud detection, smart systems, traffic monitoring, and advertising. Machine learning enhances these applications with intelligent decision-making. This course covers real-time stream processing, batch processing, graph-based processing, and ML system design. Topics include real-time event-driven stream processing, batch and mini-batch stream processing, graph-based stream processing, and key components of ML systems such as scheduling, training, and inference. Course explores platforms like Spark Streaming, Flink, Kafka, Ray, TensorFlow, and PyTorch to understand their integration at scale.
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University of California, Santa Cruz
Reviews, study tips, and grade data for CSE 232B at UCSC. Stream processing enables real-time analysis of continuous data streams, powering applications like…
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