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Published Release Notes

Find release notes for the selected Pega Version and Capability

Browse resolved issues for Platform releases.

This documentation is for non-current versions of Pega Platform. For current release notes, go here.

Fast-track change requests for high-priority business needs

Valid from Pega Version 8.3

Perform urgent rule updates through fast-track change requests. The new type of change requests supports addressing high-priority business needs and deploying them immediately, without the need to disrupt an ongoing revision.

For more information, see Release urgent business rule updates through fast track change requests (8.3) and Creating application overlays.

Customizable fonts for the native components of the mobile app

Valid from Pega Version 8.3

The mobile app executable files now include font packages, which provides more flexibility in replacing and customizing the fonts. You can now style the native header and the bottom bar more effectively, and improve readability or branding. In native elements, both text and icons support custom fonts.

For more information, see Configuring a custom font for the bottom bar.

References to parameters in text control

Valid from Pega Version 8.3

The enhanced Text control now supports referencing the parameters in the Property field, and a more flexible application of the Field Value, that you can now use in the Value field. As a result, you can now use more development options, increase reuse, and more accurately reference specific data.

For more information, see Adding a Text control.

Pagination for repeating dynamic layouts

Valid from Pega Version 8.3

Repeating dynamic layouts now support pagination, so that you can display extensive sets of data in smaller portions. Additionally, you can add custom icons or messages to inform users whether more data is available.

For more information, see "Pagination" in Configuration options for layouts.

Support for custom database tables in external Cassandra clusters

Valid from Pega Version 8.3

Pega Platform™ now supports a connection to external Cassandra clusters through a dedicated Database Table data set, which reduces the need for data ingestion and export. You can use custom tables that you store in your external Cassandra cluster in data flows for accessing and saving data. You can access your custom data model by mapping the model to a Pega Platform class.

For more information, see Connecting to an external Cassandra database through a Database Table data set.

Text analytics models editing and versioning

Valid from Pega Version 8.3

Pega Platform™ now supports editing and updating training data for text analytics models.

Pega Platform also supports the versioning of text analytics models. When you update the model, Prediction Studio creates an updated model version. You can then switch between the model versions.

Upgrade impact

In versions of Pega Platform earlier than 8.3, the training data for text models was stored in the database. In Pega Platform version 8.3 and later, the training data for text models is stored in Pega Repository. You cannot build new models without setting the repository. After the repository is set, all text models are automatically upgraded and will work normally.

What steps are required to update the application to be compatible with this change?

After a successful upgrade, set the repository in Prediction Studio before building or updating any Natural Language Processing (NLP) models.  In Prediction Studio, click Settings > Text Model Data Repository.

 

For more information, see:

 

Text analytics models migration

Valid from Pega Version 8.3

Pega Platform™ now supports the exporting and importing of text analytics models. For example, you can export a model to a production system so that it can gather feedback data. You can then update the model with the collected feedback data to increase the model's accuracy.

Upgrade impact

In versions of Pega Platform earlier than 8.3, the training data for text models was stored in the database. In Pega Platform version 8.3 and later, the training data for text models is stored in Pega Repository. You cannot build new models without setting the repository. After the repository is set, all text models are automatically upgraded and will work normally.

What steps are required to update the application to be compatible with this change?

After a successful upgrade, set the repository in Prediction Studio before building or updating any Natural Language Processing (NLP) models.  In Prediction Studio, click Settings > Text Model Data Repository.

 

For more information, see:

Prediction Studio notifications

Valid from Pega Version 8.3

Prediction Studio now displays notifications when adaptive or predictive models encounter problems. For example, if the performance of a model decreases suddenly, a notification appears, which you can use to immediately investigate the cause of the problem.

For more information, see Accessing Prediction Studio notifications and Prediction Studio notification types.

Production data sampling and migration for decision strategy simulations

Valid from Pega Version 8.3

Pega Platform™ now supports the sampling and migration of data from the production environment to the simulation environment. The data that is migrated for simulations includes customer details, Adaptive Decision Management information, and interaction history. By running a simulation on that sample data in Pega Marketing™ or Pega Customer Decision Hub™, you can verify how the changes to your decision logic impact the results. You can use that information to optimize and adjust your decision algorithms and processes.

For more information, see Deploying sample production data to a simulation environment for testing.

Integrate text analytics with decision strategies through the Interaction API

Valid from Pega Version 8.3

The Interaction API provides more context for making next-best-action decisions by integrating text analytics with decision management components such as strategies, propositions, and interaction history. By including natural language processing in your decisioning solution through the Interaction API, you ensure that the next-best-action decisions that you make are more informed and accurate.

For more information, see Customizable Interaction API for text analytics.

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