Managing Machine Learning Models

About this course

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 a model scoring job 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 Text Analytics models. This course is appropriate for 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