Published Release Notes

Support for predictive models in PMML version 4.4

Pega Platform™ now supports the import of predictive models in Predictive Model Markup Language (PMML) version 4.4. With this feature, you can import PMML models that use the anomaly detection algorithm.

For a list of all supported PMML models, see Supported models for import. 

Condition builder enhancements

To enhance the precision of condition builder in both App Studio and Dev Studio, the new instances include comparator helps you specify the number of field group and field group list instances to which a when rule applies. In addition, a search option that returns results on keypress helps you easily look for existing values.

For more information, see Create conditions in an enhanced condition builder (8.5), Defining conditions in the condition builder.

Support for Apache HBase 2.1 and Hadoop 3.0

Support for these versions extends Pega Platform™ compatibility with HBase releases to ensure that your database implementations integrate seamlessly with Pega Platform.

Pega Platform now supports:

• Apache HBase 2.1 for the HBase data set
• Apache Hadoop Distributed File System (HDFS) 3.0 for the HDFS data set

For more information, see Enhance your data sets with Apache HBase 2.1 and Hadoop 3.0 (8.5).

Enhancing your revision management process with Deployment Manager pipelines

Pega Platform 8.5 offers improved synergy between revision management and the automated deployment process provided by Pega's Deployment Manager 4.8 pipelines. Use Deployment Manager 4.8 to increase the efficiency of business-as-usual application changes and automatize the deployment of revision packages.

For more information, see Managing the business-as-usual changes.

Support for Cloud AutoML topic detection models

In Prediction Studio, you can now connect to topic detection models that you create in Cloud AutoML, Google's cloud-based machine learning service. You can then use the models to categorize and route messages from your customers.

For more information, see Broaden your selection of topic detection models by connecting to third-party services (8.5).

Control group configuration for predictions

You can now configure a control group for your predictions in Prediction Studio. Based on the control group, Prediction Studio calculates a lift score for each prediction that you can later use to monitor the success rate of your predictions.

For more information, see Customizing predictions.

Response timeout configuration for predictions

You can now set a response timeout for your predictions in Prediction Studio. By setting a response timeout, you control how Prediction Studio registers customer responses that later serve as feedback data for your predictions.

For more information, see Customizing predictions.

Support for nested Declare Trigger rules

Pega Platform™ now supports nested Declare Trigger rules so that you can conveniently create correlations between actions in your case types. Declare Triggers rules invoke an action when a specified event takes place in a case type. You can now design more complex scenarios faster by nesting more Declare Trigger rules that work in the context of running another Declare Trigger rule. For example, when a case participant changes a postal code in their personal details, a Declare Trigger rule runs and a respective customer service representative (CSR) receives an email. After the CSR receives the email, a nested Declare Trigger rule runs and your application creates a document with the updated personal details and attaches it to the case.

For more information, see Develop applications faster with nested Declare Trigger rules (8.5), Declare Trigger rules.

Optimization check utility available for legacy strategies

Ensure that your strategies are compatible with the optimized strategy execution engine introduced in Pega Platform™ 8.1 by running a post-upgrade utility that checks strategies within your application for areas that you can optimize, for example, by reducing the number of page properties that are copied from one shape to another. Running the utility produces a report that you can use to plan the required updates to your strategies.

For more information, see Make your strategies compatible with the optimized strategy execution engine by using a check utility.

More flexibility for Date Time predictors in adaptive models

You can configure adaptive model predictors that indicate the amount of time that has elapsed since a particular event. In versions of Pega Platform™ earlier than 8.5, you could only specify the absolute date and time, for example, the date when a subscriber last visited one of your brick-and-mortar store locations. In Pega Platform 8.5, you can also indicate the amount of time that has passed, for example, the number of days since a subscriber purchased a new service plan.

For more information, see Adding a predictor to an adaptive model.