Many companies are experimenting with machine learning to see how they can better predict the future. Supervised learning techniques leverage existing datasets with a target variable and apply algorithms against the features to determine how to predict the target. Data is important as your model will only be as good as your data. Once the model is built, deploying it in a reactive microservice architecture can provide many benefits including asynchronous processing along with distributed computing. One way to build this is to leverage the Akka framework. Actors can intercommunicate with each other asynchronously while also publish and subscribing to external actor systems via an event stream like Kafka. Kafka provides a resilient event stream that also enables back pressure with persistence. Leveraging all of these technologies together can provide a powerful solution that can handle concurrency and scale. In this session, we'll show you the use cases where using this integration pattern makes sense along with doing a live demo.