This text discusses the potential use cases for machine learning in mission-critical real-time applications, leveraging Apache Kafka as a central, scalable nervous system. The emergence of the Internet and smartphones has changed how people behave, leading to increased expectations for information in real-time. Traditional enterprises can implement powerful real-time processing for their daily business, often requiring domain knowledge to understand the scenario and build new streaming analytics to add business value. Machine learning is a game-changer in this context, allowing computers to find hidden insights without being explicitly programmed where to look. It enables analyzing unstructured data, image recognition, speech recognition, and intelligent decision-making. The text highlights how Apache Kafka can be used as a scalable, distributed messaging broker to feed, build, apply, and monitor analytic models, providing benefits such as scalability, performance, and mission-critical operations. It also discusses the development lifecycle of analytic models, reference architectures for building, operating, and monitoring analytic models with Kafka, and examples of deploying analytic models to production using Apache Kafka's Streams API.