Skip to main content

         This documentation site is for previous versions. Visit our new documentation site for current releases.      

Improve your custom predictive models through machine learning as a service (8.3)

Updated on May 3, 2021

In Pega Platform™, you can now improve custom predictive models that you build externally by running them through machine learning as a service (MLaaS) tools. With this functionality, you can make better customer-related decisions by using the enhanced predictive power of advanced artificial intelligence and machine learning models, including deep learning for TensorFlow, scikit-learn, and XGBoost algorithms.

To fully harness the results of your custom state-of-the-art predictive models through MLaaS, Pega Platform now provides an option to configure a connection to Google AI Platform to run such advanced algorithms externally. The following videos illustrate how you can connect to an external predictive model and then use the results in Pega Platform.

"Configuring a machine learning service"
Configuring a machine learning service
"Connecting to a machine learning model"
Connecting to a machine learning model

For more information, see Connecting to a Machine Learning as a Service model and Configuring a machine learning service connection.

  • Previous topic Improve the management of text extraction models through entity types (8.3)
  • Next topic Process high-volume interactions more efficiently (8.3)

Have a question? Get answers now.

Visit the Support Center to ask questions, engage in discussions, share ideas, and help others.

Did you find this content helpful?

Want to help us improve this content?

We'd prefer it if you saw us at our best. is not optimized for Internet Explorer. For the optimal experience, please use:

Close Deprecation Notice
Contact us