Real-time machine learning applications on human mobility data continue to grow, thanks to the increase in digital mobility data, such as phone records and GPS traces. This has led to exponential growth in areas such as next location prediction, crowd flow, trajectory generation, flow generation, disease spreading, urban projection, and well-being. However, these predictions have many challenges such as combining multiple data sources from multiple devices in real-time, security and privacy measurements, the ability to apply machine learning on a large scale without data loss or failovers, and most importantly, real-time event stream processing to provide instant reactions. In this talk, you will learn how you can apply real-time machine learning on multiple human mobility datasets to provide real-time predictions, using Hazelcast, the Open-Source real-time data platform. The talk will address these challenges and provide solutions and best practices on how to optimize real-time machine learning predictions on human mobility datasets in the following areas: scalability, performance, failover, reliability, and data recovery. Resources: