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InRule Technology, a specialist in providing AI-enabled automation software for enterprise IT systems, has incorporated standards-based AutoML into xAI Workbench, its suite of machine-learning modeling engines. 

The Chicago, Illinois-based company said that incorporating automated machine learning into its no-code decision platform enables enterprises to scale the use of machine learning throughout the organization faster and more effectively. Speed is of the essence in producing ML models because of the sheer amount of data to be processed for many use cases. 

XAI Workbench enables teams to develop a wide variety of machine learning models at massive scale, InRule AI & ML product manager Danny Shayman told VentureBeat. The suite of modeling engines provides solutions that produce any combination of similarity search, classification, clustering and recommendation. XAI Workbench enables “fairness through awareness” with built-in bias detection features designed to help enterprises quantify and mitigate potential risk, Shayman said.

By providing a guided, no-code model-building process, Shayman said, AutoML helps users solve machine learning challenges faster, using fewer resources and staff time, while at the same time reducing the risk of human error and increasing the accuracy of predictions.

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AI is for human empowerment

“You know, the purpose of any of these AI systems is simply human empowerment,” Shayman said. “If you’re using a bot on some website, your objective isn’t to necessarily make the bot as human as possible – it is to make the bot as effective as possible at conveying the information that needs to be conveyed.” That requires the AI / ML systems to be accessible and comprehensible to the person using it, he explained.

Machine learning has achieved considerable success in recent years, and an ever-growing number of disciplines rely on it. However, this success still relies on human machine-learning experts to perform the following tasks:

  • Preprocess and clean the data
  • Select and construct appropriate features
  • Select an appropriate model family
  • Optimize model hyperparameters
  • Design the topology of neural networks (if deep learning is used)
  • Post-process machine learning models, and
  • Critically analyze the results obtained

Because the complexity of these tasks is often beyond non-ML-experts, the rapid growth of machine learning applications has created a demand for off-the-shelf machine learning methods that can be used easily and without expert knowledge. The resulting research area that targets progressive automation of machine learning became AutoML.

‘Intelligent automation platform’

“We look at ourselves as an intelligence automation platform,” CEO Rik Chomko told VentureBeat. “So we’ve got the ability to move decisioning for anyone to processes in a way that that is sort of seamless with how your enterprise works today, from an IT perspective. We have a decided focus on letting the non-technical users participate in that whole process. So we’re sort of a low code for intelligent automation.”

In a media advisory, InRule VP of engineering and data science David Jakopac also said that for organizations seeking to empower non-data science staff to build machine learning models, “the code-free AutoML workflows within xAI Workbench make building and deploying highly performant machine learning models fast, safe and accessible.”

XAI Workbench enables teams to develop a wide variety of machine learning models at massive scale. The suite of modeling engines provides solutions that deliver any combination of similarity search, classification, clustering, and recommendation. XAI Workbench enables fairness through awareness with built-in bias detection features designed to help enterprises quantify and mitigate potential risk.
InRule competes in the same market as PegaPlatform, Kissflow, UiPath RPA, Nintex Process Platform and OutSystems, according to industry analyst G2.

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