Published Release Notes

Predictive models can drive predictions

You can use predictive models as the basis of your predictions. As a data scientist, you can now use Machine Learning Operations (MLOps) to replace models in your system. You can replace a model in a prediction with a PMML, H2O MOJO, or Pega OXL predictive model, as well as a scorecard or field, and then approve the update for deployment to a production environment. You can respond to a Prediction Studio notification that an active model does not generate enough lift and decide to replace the low-performing model with a high-accuracy model. You can also update a prediction on a regular basis, for example, whenever you develop a new churn model in an external environment.

For more information, see Replace models in predictions and migrate changes to production with MLOps.

Enhancing the performance of your Next-Best-Action strategy with globally optimized strategies

Starting in version 8.6, Pega Platform™ combines the versatility of Next-Best-Action Designer with strategy performance enhancements provided by using globally optimized strategies (GOS). Decrease the run time and memory usage of executing the Next-Best-Action strategy in batch or real-time scenarios by using the globally optimized strategies generated by Next Best Action Designer.

GOS is supported by Pega Platform's standard business change management process. GOS rules are automatically included in relevant revision packages.

For more information, see Enhance the performance of your Next-Best-Action strategy with globally optimized strategies (8.6).

Support for picklists with parameterized data pages in App Studio in Cosmos React UI

You can now use data pages with parameters to populate a property of the picklist type with filtered results in App Studio. For example, in a survey case type, you can use a parameterized data page to configure cascading drop-down controls in which the values in a secondary drop-down list are based on the value that the user selects in the primary drop-down list. With dynamically-sourced picklists, you get greater flexibility in configuring picklists, and users see more accurate values.

For more information, see link Dropdown control Properties — General tab.

Improved security after hiding SQL values

In Pega Platform™ version 8.6, the default value of the showSQLInList dynamic system setting changes to FALSE, which suppresses the visibility of the SQL values in the Clipboard tool. With this setting disabled, when you run a report definition the clipboard does not display sensitive values in the pxSQLStatementPre and pxSQLStatementPost properties, which makes your system less vulnerable. If you want to display the values, in the .prconfig file, set the prconfig/security/showSQLInListPage/default to TRUE.

Additional connection pool parameters for JDBC URL external databases

For improved external JDBC URL database connection performance, Pega Platform™ now supports multiple Hikari connection pool parameters directly in the Data-Admin-DB-Name rule. Previously, Pega Platform supported a limited number of connection pool parameters to optimize your database connection using dynamic system settings (DSS). Now you can optimize your JDBC URL connection pool sizing to meet your traffic requirements through additional parameters without switching rules. Additional parameters include client-request parameters to maximize the connectivity to your external databases.

For more information, see Creating and updating external database instances with JDBC URLs.

Upgrade impact

During an upgrade to Pega Infinity™ release 8.6 and later, clients who previously specified their JDBC URL connection parameters through dynamic system settings (DSS) or prconfig file must delete the corresponding DSS or prconfig elements immediately after upgrade. The old parameters that you previously specified through DSS or the prconfig file override the equivalent parameters that you specified in the JDBC URL rule after your upgrade.

For more information about deleting your previous DSS or prconfig elements, see Deleting dynamic system setting connection parameters and Deleting connection parameters in the prconfig file.

Enhanced PEGA0104 alert

The PEGA0104 alert now includes the thread pool size, a list of activities that are waiting in the queue for a thread to become available, and details on how long it took to capture the thread dump.  By default, the alert also includes the thread dump.  You can disable the inclusion of the thread dump by changing the related configuration option. 

For more information, see PEGA0104 alert: Background tasks have exceeded a threshold time when attempting to acquire a thread from pool or executor.

New alert for job scheduler activity that exceeds a time threshold

Pega Platform™ now saves the PEGA0130 alert in the performance alert log at the time that a scheduled job exceeds the configured threshold value. To identify performance issues with long-running processes, you can monitor the log for the alert while the job scheduler runs.

The alert is enabled by default. You can change the default threshold value or disable the alert in dynamic system settings.

For more information, see PEGA0130 alert: Current Job Scheduler run is taking too long.

Easier customer record management in Customer Profile Designer (early preview)

The new Customer Profile Designer module of Pega Customer Decision Hub™ makes it possible for marketing analysts and strategy designers to define the associated data for each customer context directly in the Pega Customer Decision Hub portal. It is also possible to define more complex associated data structures that use a custom data flow, or define associated data of different types, such as RDBMS and Cassandra, for the same customer context.

Customer Profile Designer is available in Pega Customer Decision Hub 8.6 version as an early preview version. The functionality will be further expanded in future releases.

For more information, see Manage customer records in Customer Profile Designer (8.6).

Text predictions simplify the configuration of text analytics for conversational channels

Enable text analytics for your conversational channels, such as email and chatbot, by configuring text predictions that manage the text models for your channels. This new type of prediction in Prediction Studio consolidates the AI for analyzing the messages in your conversational channels in one place and replaces the text analyzer rule in Dev Studio.

Through text predictions, you can efficiently configure the outcomes that you want to predict by analyzing the text in your channels:

• Topics (ticket booking, subscription cancellation, support request)
• Sentiments (positive, neutral, negative)
• Entities (people, organizations, airport codes)
• Languages

You can train and build the models that predict these outcomes through an intuitive process, and then monitor the outcomes through user-friendly charts.

For more information, see Predict customer needs and behaviors by using text predictions in your conversational channels.

Upgrade impact

Channels that you configured with text analyzers in the previous version of your system continue to work in the same manner after the upgrade to the current version. When you edit and save the configuration of an existing channel, the text analyzer rule is automatically upgraded to a text prediction. The associated text prediction is now an object where you can manage and monitor the text analytics for your channel. When you create a new channel in the upgraded system, the system automatically creates a text prediction for that channel.

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

1. Enable the asynchronous model building and reporting in text predictions through job schedulers that use the System Runtime Context (SRC) by adding your application to the SRC.
   For more information, see Automating the runtime context management of background processes.
2. Enable model building in text predictions by configuring background processing nodes.
   For more information, see Assigning decision management node types to Pega Platform nodes.

Kafka data set enhancements

The Kafka data set is a high-throughput and low-latency platform for handling real-time data feeds that you can use as input for Pega Platform event strategies.

For better integration of Pega Platform with externally hosted Kafka, the following enhancements are implemented:

• Support for Kafka message keys and headers - extended values data format (JSON Data Transform, Apache Avro)
• Custom value processing
• Configuring topic names by using Application Properties
• Data-Admin-Kafka enhancements - supporting a wide range of connection properties

For more information, see Improve your Kafka data set with new enhancements.