<ul><li>This article discusses the development of a real-time fraud detection pipeline using Apache Kafka and Python.</li><li>The pipeline consists of a producer that streams transaction data to Kafka, a feature processor that scales and preprocesses features, a fraud detector that uses a trained K-Nearest Neighbors (KNN) model to predict fraud, and an alert system that logs suspicious transactions and provides metrics and visualizations.</li><li>Key components of the pipeline include Docker Compose for container orchestration and the use of Kafka topics for data streaming.</li><li>The results show impressive performance, with some fraud alerts clocking in under 30 milliseconds, an average inference time of less than 500 milliseconds, and a peak throughput of 1200 transactions per minute.</li></ul>

Blazing Fast Fraud Detection with Kafka (<500ms, No Kidding)

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