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Published Release Notes

Find release notes for the selected Pega Version and Capability

Browse resolved issues for Platform releases.

This documentation is for non-current versions of Pega Platform. For current release notes, go here.

Support for predictive models in PMML version 4.4

Valid from Pega Version 8.5

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

 

Reports with visualizations of performance and automation levels for the email bot

Valid from Pega Version 8.5

In the Email Manager portal, you can now use built-in reports to visualize Pega Email Bot™ performance and the automation levels that are related to received emails, triaged cases, and created business cases. You can plug the reports to display the report data in other portals, for example the Case Manager portal. Based on the information displayed in the reports, you can adjust the machine learning models to achieve greater automation in the system.

For more information, see Viewing the reports for the Email channel and Built-in reports for the email bot.

Improved experience when building an IVA and Email Bot in App Studio

Valid from Pega Version 8.5

Build your Pega Intelligent Virtual Assistant™ (IVA) and Pega Email Bot™ while working only in App Studio. This approach makes the design process easier and more intuitive, and saves you time. You can now modify the advanced text analyzer configuration while working in App Studio. In addition, if you have access to Dev Studio, you can edit the text analyzer rule from App Studio for your chatbot or email bot by clicking a link to open the settings in Dev Studio.

For more information, see Adding a text analyzer for an email bot and Adding a text analyzer for an IVA.

Triage cases archiving in the email bot (Pega Cloud Services)

Valid from Pega Version 8.5

For Pega Platform™ that is installed in Pega Cloud® Services, you can configure Pega Email Bot™ to archive resolved triage cases that are older than a specified number of days. Archiving triage cases improves the overall performance of your system by reducing the primary storage consumption and cost because the system places such resolved triage cases in a secondary storage.

For more information, see Archiving resolved emails for an email bot (Pega Cloud Services).

Limits on active data flow runs

Valid from Pega Version 8.5

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

Valid from Pega Version 8.5

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).

Usability improvements in the Email Manager and Case Manager portals

Valid from Pega Version 8.5

Pega Email Bot™ customer service representatives (CSRs) working in the Email Manager, Case Manager, or Case Worker portals can now quickly reply to one recipient or to all recipients. CSRs can also view the sentiment analysis of an email (positive, negative, or neutral) for each received email, displaying the sentiment pattern for the entire email thread in a triage case. The email bot improvements add value for CSRs working in the portals and help them save time when responding to user requests.

For more information, see Understanding the email triage process and Replying to customers by email for an email bot.

Enhancing your revision management process with Deployment Manager pipelines

Valid from Pega Version 8.5

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

Valid from Pega Version 8.5

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

Valid from Pega Version 8.5

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.

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