Predictive models can drive predictions
Valid from Pega Version 8.6
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
Valid from Pega Version 8.6
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
Valid from Pega Version 8.6
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)
Valid from Pega Version 8.6
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).
Kafka data set enhancements
Valid from Pega Version 8.6
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.