The foundations of machine learning were built on offline batch processing techniques for model training and inference. As organisations become more dependent on real-time data, the technological trend for machine learning in production is moving towards adding an online stream processing approach. This has benefits such as lower computational requirements due to being able to incrementally learn from a stream of data points, which enables the continual upgrading of models by adapting to real-time changes in data. This has wide applications in industries such as cyber security, banking, healthcare, IIoT and any industry that involves processing large volumes of high throughput data and adapting predictive capability with real-time data feeds. This capability was once only in the realm of Java developers but now it's available to Python developers. You’ll leave this talk with an understanding of the differences between offline and online machine learning, how to complement one with the other and enough streaming concepts and best practices needed get started on your online ML journey with River, an open source Python ML library.