Build and Deploy Scalable Machine Learning in Production with Kafka

What this post added

This post details how to build, operate, and monitor analytic models in a scalable, mission-critical way by leveraging Apache Kafka as a central, scalable, mission-critical nervous system and Apache Kafka's Streams API to build intelligent streaming applications. It outlines the machine learning development lifecycle (Build, Validate, Operate, Monitor, Continuous Loop) and presents a reference architecture using Kafka as an intermediary for feature data collection, model building, and prediction serving. It also provides a specific example architecture separating model training (using Hadoop and H2O.ai) from model inference (using Kafka Streams), and briefly mentions online model training as an alternative.