3 Kafka patterns to deliver Streaming Machine Learning models

The presentation highlights the main technical challenges Radicalbit faced while building a real-time serving engine for streaming Machine Learning algorithms. The speech describes how Kafka has been used to fasten two ML technologies together: River, an open-source suite of streaming machine learning algorithms, and Seldon-core, a DevOps-driven MLOps platform. In particular, the talk focuses on how Kafka has been used to (1) build a dynamic model serving framework thanks to Kafka Streams joins and the broadcasting pattern (2) implement a Kafka user-given feedback topic by which online models can learn while they generate predictions, and (3) design a models' prediction bus, a particular Kafka bidirectional topic whereby predictions flow at tremendous scale; the prediction bus enabled seldon-core Kubernetes deployment to communicate with Kafka Streams, and as a conclusive subject this speech explains how this unleashed unprecedented performance.