Real-Time Machine Learning is a technique of continuously improving a machine learning model by training it with real-time rich data. Real-time machine learning models can be deployed to production using event-driven architectures in which rich data streams are fed into these models by combining data-at-rest with data-in-motion. However, deploying real-time machine learning models has its own challenges: such as complexity, scalability and performance. In this seminar, Fawaz will address those challenges and demonstrate the best practices for real-time machine learning models, written in Python, Java or using remote services, from training to inference and deployment with ultra-low latency at scale and at speed, using the Hazelcast platform.