As industry media continues to drive the popularity of the term “artificial intelligence,” it leaves many unanswered questions. How will software teams deal with the increased demand to “add more AI” to their projects? Where do your domain experts add their experiences to the algorithms? How can business processes be improved through machine learning?
Rebecca Simmonds and Michael McCune offer answers to all these questions. You’ll see how to combine the worlds of data science and business process management to form processing engines that deliver reduced overhead and improved automation and learn how distributed machine learning frameworks fit together naturally with business rules management systems. Join in to discover how to enhance your rules-based processes with machine learning-inspired analysis.
Rebecca Simmonds is a senior software engineer at Red Hat, where she’s part of an emerging technology group comprising both data scientists and developers. She has a keen interest in architecture design and data analysis, which she is furthering at Red Hat with Openshift and ML research. Previously, she was a Java developer creating solutions to improve performance for a CV analyzer at a small startup. She holds a PhD from Newcastle University, where she developed a platform for scalable geospatial and temporal analysis of Twitter data.
Michael McCune is a software developer in Red Hat’s Emerging Technology Group, where he develops and deploys application for cloud platforms. He’s an active contributor to several radanalytics.io projects and a core reviewer for the OpenStack API Working Group. Previously, Michael developed Linux-based software for embedded global positioning systems.
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