This applied, hands-on course teaches you how to manage models through their useful life cycle. After creating a modeling project, you add and compare models to it so that you can identify a champion model. The course uses models that are created using SAS Advanced Analytics capabilities, Python, and R. The course also shows how to implement workflow to ensure that model governance and oversight approval is being followed.
You learn how to test a model in the production environment in which it will be deployed. After the model test completes successfully, you learn how to schedule the model so it can run automatically.
Further, the course shows how to measure and monitor the ongoing model performance over time. The performance monitoring process will also be scheduled to run automatically in class.
An optional lesson shows how to register and score SAS Visual Text Analytics models.
Learn How To
Manage SAS Model Manager data sources. Import models into SAS Model Manager. Score SAS Model Manager models. Create SAS Model Manager performance reports. Schedule Model Manager jobs.Who Should Attend
Anyone involved in data preparation and production model scoring; modelers who create and test models; business analysts who are consumers of the model; and business analysts or consultants who are responsible for integrating models, business rules, and rule flows into operational processes
Prerequisites
Before attending this course, you should be familiar with data mining concepts and predictive models.
SAS Products Covered
SAS Model Manager
Course Outline
Why Manage Models?
The analytical live life cycle.Key user roles.Managed model life cycle.Development operations pipeline.Model operations.Model operations environment.Working with Projects and Models Introduction. Project setup. Import models and model properties.Working with Python models.Working with R models.Evaluate models.Import, enable, and use a workflow.Model DeploymentIntroduction. Publishing models. Defining a CAS publishing destination. Scoring deployment. Creating a Model Performance report. Scheduling a performance job. Model retraining (self-study).Scoring SAS Visual Text Analytics Models (Self-Study)Introduction.Scoring SAS Visual Text Analytics models. AppendixModel repositories.How to fit a scoring script for model containerization.Preparing an R model and PMML file.Calculating fit statistics for an R model.Feature contribution index.Model usage summary.
Live Instructor Dates 