SAS Machine Learning Engineer

This course covers the theoretical foundation for different techniques associated with supervised machine learning models. In addition, a business case study is defined to guide participants through all steps of the analytical life cycle, from problem understanding to model deployment, through data preparation, feature selection, model training and validation, and model assessment. A series of demonstrations and exercises is used to reinforce the concepts and the analytical approach to solving business problems. This course uses Model Studio, the pipeline flow interface in SAS Viya that enables you to prepare, develop, compare, and deploy advanced analytics models. You learn to train supervised machine learning models to make better decisions on big data. The SAS applications used in this course make machine learning possible without programming or coding.

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

This course includes access to three practice exams to help you evaluate your readiness to sit for the following SAS Certification exams: Applied Statistics for Machine Learning Associate, Machine Learning Using SAS(R) Viya(R), ModelOps Specialist.