Published Release Notes

Improvements for the PMML models

The Pega 7 Platform extends support for the PMML models to include PMML 4.2 features and validations. Additionally, the PMML files that contain custom functions can be imported into the Pega 7 Platform and used in strategies.

For more information, see Predictive Model rule form.

Improvements to the adaptive models management

There are several improvements for managing large sets of adaptive models that make model management faster and easier. In the Adaptive Models Management landing page, you can clear and delete models in bulk. In the Predictors tab of the Adaptive Model rule, you can add multiple predictors. In the Memory estimation tab of the Adaptive Model rule, you can estimate the memory that the Adaptive Decision Manager (ADM) system needs to accommodate the models that use the adaptive model configuration.

For more information, see:

• Adaptive Models Management landing page
• Adaptive model form - Completing the Predictors tab
• Adaptive model form - Complating the Memory estimation tab

Improvements to the HBase data set, HFDS data set, and Hadoop host configuration

The Pega 7 Platform features maintenance improvements to the Hadoop host configuration, HBase data set, and Apache Hadoop File System (HDFS) data set. System architects can more easily make HDFS and HBase storage available to business scientists for exploratory analysis and predictive model building.

For more information, see Big data functionality enhancements.

Data Flow rule enhancements

The enhancements to the Data Flow rule facilitate decision execution in real time, batch, and Pega 7 Platform applications. The DataFlow-Execute method in activities can be used to execute and monitor batch runs and real time data flows. In addition, you can configure pre- and post-activities that are executed before and after data flow execution.

For more information, see the following Pega 7 Platform help topics:

• About data flow rules
• DataFlow-Execute method
• Pre- and post-processing of data flows

Strategy canvas enhancements

The Strategy canvas in Decision Management has been enhanced to let you to create, edit, and display your strategy more easily. You can keep your strategy in order using the Smart Mini Map, improved selection capability, and the new Align and Distribute options. Additionally, you can perform the most common operations using keyboard shortcuts and accelerators.

For more information, see Strategy rule enhancements.

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.

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.