Introduction to Predictive Learning¶

Predictive Learning (PrL) enables data scientists to develop and deploy custom machine learning (ML) algorithms using statistical functions, transformations, and data filtering capabilities. PrL provides access to both historical and near real-time data through multiple data sources, including:

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

Core Capabilities¶

PrL provides developers and data scientists with the following capabilities:

• Validate and execute models within managed environments
• Create Jupyter notebooks and save them locally while environments continue to run
• Use Python scripts to read data from APIs
• Deploy locally built models for execution as Docker containers
• Schedule model execution workflows
• Connect to various data sources for input and output operations

User Roles and Access Control¶

Predictive Learning inherits user roles and permissions from the Settings application on the Launchpad. For detailed information on user role assignment, refer to user management and roles. If the Settings application is not visible on the Launchpad, contact your system administrator.

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