Using Apache Kafka to Drive Cutting-Edge Machine Learning | Confluent

What this post added

This post expands on the use of Kafka for machine learning by detailing specific implementation patterns and architectural considerations. It elaborates on embedded model deployment within Kafka Streams applications and via RPC to dedicated model servers, discussing their respective tradeoffs. It also details hybrid cloud architectures for training and inference, and the use of KSQL for preprocessing and embedding ML models via User-Defined Functions (UDFs), including an example of a deep learning UDF for anomaly detection.