Published Release Notes

Integrate text analytics with decision strategies through the Interaction API

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.

Support for machine learning as a service models

In Pega Platform™, you can now run advanced machine learning and artificial intelligence models that you develop in third-party tools. By configuring a connection with an external machine learning as a service provider, such as Google AI Platform, you can use the predictive power of your custom models to improve the predictions in your customer strategies.

For more information, see Connecting to a Machine Learning as a Service model and Configuring a machine learning service connection.

Stream-based alternative for interaction history

Accelerate the processing of high-volume interaction history data by incorporating a stream-based interaction history into your application. A stream-based interaction history processes large volumes of data more quickly than a traditional relational database interaction history so that you can react to your customers' needs in real time.

For more information, see Process high-volume interactions more efficiently and Aggregates-only mode for a stream-based interaction history.

Aggregates-only mode for a stream-based interaction history

Activate the aggregates-only mode to efficiently transition your application from a relational database interaction history to a stream-based interaction history. By transitioning from a traditional relational database interaction history to a stream-based interaction history, you enable your application to process large volumes of data more quickly so that you can react to your customers' needs in real time.

For more information, see Transitioning to a stream-based interaction history.

Ensure event strategy reliability through unit tests

You can now automate event strategy testing and increase the reliability of the event strategy configuration through unit tests. Avoid errors and ensure that the strategy delivers the expected results by configuring assertions on property values, aggregates, and result counts.

For more information, see Increased event strategy reliability through unit testing.

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. 

Limits on active data flow runs

You can now configure a maximum number of concurrent active data flow runs for a node type. Set limits to ensure that you do not run out of system resources and that you have a reasonable processing throughput. If a limit is reached, the system queues subsequent runs and waits for active runs to stop or finish before queued runs can be initiated, starting with the oldest.

For more information see, Limit the number of active runs in data flow services (8.5).

Upgrade impact

If you have many data flow runs active at the same time, you might notice that some of the runs are queued and waiting to be executed.

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

You do not have to take any action. After the active runs stop or finish, the queued runs start automatically. The default limits are intended to protect your system resources, and you should not see a negative impact on the processing of data flows. However, if you want to allow a greater number of active data flow runs to be active at the same time, you can change the limits. For more information, see Limiting active data flow runs.

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).