Learning from Events in Real-Time
Learn how to apply statistical learning techniques to real-time event-driven data in Python by integrating distributed machine learning models with scalable, high-throughput and fault-tolerant streaming platforms.
This course provides a hands-on exploration of the industry-standard Apache Kafka distributed streaming platform and how it can be integrated with distributed machine learning models via Apache Spark and its Structured Streaming engine in order to build high-throughput and low-latency real-time machine learning systems. This course follows on from our Applied Machine Learning and Distributed Machine Learning courses, and enables experienced senior data scientists and data engineers to learn from event-driven data and make predictions in real-time. This course also provides guidance on real-time architectural patterns, as well as how to build real-time continuous feedback loops in order to automate the training of machine learning models based on the actions of system users and customers.