As the size and variety of your engineering data has grown, so has the capability to access, process, and analyse those (big) engineering data sets in MATLAB^®. With the rise of streaming data technologies and large-scale cloud infrastructure, the volume and velocity of this data has increased significantly, and this has motivated new approaches to handle data-in-motion. This presentation and demo highlights the use of MATLAB as a data analytics platform with best-in-class stream processing frameworks and cloud infrastructure to express MATLAB based workflows that enable decision-making in “near-real-time” through the application of machine learning models. It demonstrates how to use MATLAB Production Server™ to deploy these models on streams of data from Apache^® Kafka^®. The demonstration shows a full workflow from the development of a machine learning model in MATLAB to deploying it to work with a real-world sized problem running on the cloud.