Machine learning comes to Red Hat Process Automation Manager

Red Hat Process Automation Manager ML

Red Hat Inc. announced that it updated last Friday your Red Hat Process Automation Manager suite with predictive modeling capabilities based on machine learning, a variety of user interface improvements and decisions using micro frontend architectures. In combination with other enhancements aimed at improving the overall user experience for Red Hat Process Automation customers, these features strengthen the business developer's toolbox.

Red Hat Process Automation is a suite of products to automate business decisions and processes by enabling closer collaboration between IT and business teams. This helps IT organizations to better capture and enforce business policies and procedures, automate business operations and measure business results in heterogeneous environments, including physical, virtual, mobile, and cloud.

The software package, which it was previously called JBoss Business Process Management Suite, combines BPM with business rule management, business resource optimization, and complex event processing in one platform for developing container-based applications of software and microservices.

They represent an approach to create applications that combine components loose to improve endurance and accelerate development. The cloud-based suite builds cloud-native applications in container images that can be deployed to Red Hat's OpenShift container orchestration.

In the new version, the microservices architecture has been extended for the development of the user interface in an approach that the company calls "micro-frontend architectures."

Phil Simpson, Red Hat JBoss Product Marketing Manager I comment that:

"It's a way of composing a user interface from individual pieces that don't necessarily know each other."

With the latest version from Red Hat Process Automation, customers can import and run predictive models expressed in Predictive Model Markup Language (PMML), an industry standard for integrating and exchange information between machine learning platforms (ML) where predictive models are created and trained and decision management applications that use those models to automate rules for specific business outcomes.

Also complies with Business Process Model and Notation 2.0 and Decision Model and Notation 1.2, which are open standards for describing and documenting business models that are useful in model analysis and regulatory reporting.

Although Red Hat is best known for its infrastructure software, it also has a large application development business, into which Process Automation Manager fits.

"It's a relatively small component of our overall business, but one of our fastest growing areas," Simpson said.

The goal of this latest version is to make the suite more accessible to non-professional business analysts and developers.

Process Automation Manager is mainly based on Drools, an open source project developed by Red Hat that combines a business rules engine, web authoring tools and a rules management application.

With the addition of machine learning capabilities, rule-based applications can be loaded with training data allowing them to make better decisions based on past actions.

For example, in a credit approval scenario,

"They can channel years of credit decisions and create a predictive model that determines how those decisions should be made in the future," Simpson said. "Ground rules can be dramatically improved and made available to a wider range of applications" using microservices.

Other improvements in this version include automated lifecycle management support through OpenShift Operators, better process visibility through visualizations, support for continuous operation through the deployment of multiple nodes, which protects against node failures and customizable templates for the optimization of business resources.

To read the full Red Hat release, you can from the link below.


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