Integrating scalable machine learning into business workflows
Business workflows can be very rigid where the same set of actions are executed repeatedly. To have a competitive process, your workflows need to continuously learn from customers to provide a more unique and improved experience. There is now a need to use historic data available to enterprises and provide a way to automate repetitive tasks and augment the data to extract additional insights to help them understand their customers. Introducing machine learning into the workflow this can help fulfill this need.
In this mini session, we'll discuss a reference architecture for integrating machine learning as a service into business workflows to create a fraud detection artificial intelligence (AI) solution. This solution will use Red Hat Process Automation Manager and Open Data Hub as the foundational components for ingesting data, deploying machine learning (ML) models-as-a-service and implementing business workflows that integrate with the services. We'll show you how to use these tools to create an end-to-end pipeline for intelligent process automation in Red Hat OpenShift, a modern cloud deployment solution.
Enterprise integration, Future technologies
Artificial intelligence/machine learning (AI/ML), Business automation
Manage and analyze data, Design application/system architectures
Community project(s), Red Hat Process Automation Manager (Red Hat JBoss BPM Suite)
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