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. 

Automatically update dependent applications to use the latest versions of built-on applications

You can now automatically update dependent applications to use the latest versions of built-on applications. When you import an archive that you created from a product rule, you can update all dependent applications to be built on the latest application versions in the archive.

For more information, see Updating dependent applications with the latest versions of built-on applications.

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).

Alerts for long-running queue processor and job scheduler activities

Pega Platform™ now saves an alert in the performance alert log when a queue processor activity or a scheduled job runs longer than the configured threshold value. Use the alerts to identify potential performance issues with long-running processes.

The alerts are enabled by default. You can change the alerts for dedicated queue processors and job schedulers at the rules level. For standard queue processors, you can also set the threshold value for the Queue-For-Processing command in an activity. You disable the alerts in dynamic system settings.

For more information, see:

• PEGA0117 alert: Long-running queue processor activity
• PEGA0118 alert: Long-running job scheduler activity

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.

External data flow rules are deprecated

External data flows are now deprecated and no longer supported. To improve your user experience with Pega Platform™, the user interface elements associated with these rules are hidden from view by default. Identifying unused features allows Pega to focus on developing and supporting the features that you need.

For more information, see Deprecated: External data flows.