Skip to content

Introduction to Predictive Learning

Predictive (PrL) Learning enables data scientists to write or bring their own machine learning (ML) algorithms using statistical functions, transformations, and filtering to bring the most comprehensive and flexible means for accessing and working with both historical and near real-time data. PrL supports multiple data sources, such as:

  • Integrated Data Lake (IDL) : Folders in IDL can be referenced from PrL.
  • Internet of Things (IoT) : Asset data can be made available to PrL.


PrL features allow users to:

  • Validate and execute models within environments
  • Create Jupyter notebooks and save them locally while environments continue to run
  • Use Python scripts to read data from APIs
  • Bring locally build models for execution as Dockers
  • Schedule the model execution
  • Use variety of data sources for Input and Output

Roles and Permissions

Predictive Learning adopts user roles and Permission from the settings application on the Launchpad. For more information on how to assign user roles, refer to user management and roles. If the Settings application does not appear on the Launchpad, please contact your administrator.


  • Users with the Predictive Learning Admin role are allowed to create, update, delete, or view a list of environment configurations based on the available configuration template, while users with the Predictive Learning User role can only select, start, or stop an environment to run your model based on the environment configurations created and saved by your administrator.

Last update: July 17, 2024

Except where otherwise noted, content on this site is licensed under the Development License Agreement.