As the size and variety of your engineering data has grown, so has the capability to access, process, and analyze those (big) engineering data sets in MATLAB®. With the rise of streaming data technologies, the volume and velocity of this data has increased significantly, and this has motivated new approaches to handle data-in-motion. Jim Stewart discusses the use of MATLAB as a data analytics platform with best-in-class frameworks and infrastructure to express MATLAB based workflows that enable decision making in “real-time” through the application of machine learning models. He 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.
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