Skip to main content



6 Topics

1 hr

Visible to: All users
Beginner Pega Customer Decision Hub 8.8 Decision Management English
Verify the version tags to ensure you are consuming the intended content or, complete the latest version.

Machine Learning Operations (MLOps) is an approach that streamlines the process of building, testing, and deploying machine learning models. As a data scientist involved in a Pega Customer Decision Hub™ project, MLOps can help you manage the complexity of the machine learning pipeline.

In the business operation environment, you can add potential predictors to adaptive models and you can deploy new predictive models in shadow mode. In shadow mode, you can monitor the performance of a new model on production data without impacting business outcomes. Once the new model performs well, you can promote it to active status.

By utilizing MLOps best practices, you ensure that your models are robust, reliable, and integrate easily into the larger Customer Decision Hub ecosystem.

After completing this module, you should be able to:

Modify adaptive models.
Deploy a new predictive model in shadow mode.
Promote the new model to the active model status.
Promote a shadow model to the active status.

Practice what you learned in the following Challenges:

Adding predictors to an adaptive model in BOE v1 Replacing a predictive model v3 Promoting a shadow model to active status v1

Available in the following mission:

AI for 1:1 Customer Engagement v2

We'd prefer it if you saw us at our best.

Pega Academy has detected you are using a browser which may prevent you from experiencing the site as intended. To improve your experience, please update your browser.

Close Deprecation Notice