This applied, hands-on course teaches you how to manage models through their useful life cycle. You start by creating a modeling project, and then add and compare models to identify a champion model. The course uses models that are created using Python and R languages.

You learn how to test a model in the production environment to which it will be deployed.

The course also shows how to measure and monitor the ongoing performance of model accuracy over time.

The self-study e-learning includes:

• Annotatable course notes in PDF format.
• Virtual Lab time to practice.

Learn how to

• Manage data sources.
• Import models.
• Score models.
• Create performance reports.

Who should attend

IT staff who are involved in data preparation and model scoring, modelers who create and test models, and business analysts or consultants who are responsible for integrating models into operational processes

Formats available	Duration
Classroom:	1.0 day
Live Web Classroom:	2 half-day session(s)	System Requirements
e-Learning:	7 hours/180 day	System Requirements

Before attending this course, you should be familiar with data mining concepts and predictive modeling.

This course addresses SAS Open Model Manager software.

Why Manage Models?

• Introduction.

Models and Projects

• Introduction.
• Project setup.
• Import models.
• Model properties.

Model Deployment

• Introduction.
• Publishing (deploying) models.
• After deployment.
• Model performance reporting.