SAS Model Manager
| Title | Level | Training Formats |
|---|---|---|
|
Strategies and Concepts for Data Scientists and Business Analysts
To be effective in a competitive business environment, analytics professionals need to use descriptive, predictive, and prescriptive analytics to translate information into decisions. An effective analyst also should be able to identify the analytical tools and data structures to anticipate market trends. In this course, you gain the skills data scientists and statistical business analysts must have to succeed in today's data-driven economy. Learn about visualizing big data, how predictive modeling can help you find hidden nuggets, the importance of experiments in business, and the kind of value you can gain from unstructured data. This course combines scheduled, instructor-led classroom or Live Web sessions with small-group discussion, readings, and hands-on software demonstrations, for a highly engaging learning experience. |
3 Intermediate |     |
|
Managing SAS Analytical Models Using SAS Model Manager Version 14.2
This course focuses on the following key areas: managing SAS Model Manager data sources, creating a SAS Model Manager project, importing models into SAS Model Manager, using the SAS Model Manager Query Utility, creating scoring tasks, exporting models and projects into a SAS repository, and creating and configuring version life cycles. The course also covers generating SAS Model Manager model comparison reports, publishing and deploying SAS Model Manager models, creating SAS Model Manager production model monitoring reports, and creating user-defined reports. |
3 Intermediate | |
|
ModelOps: Governing AI and Machine Learning Models That Drive Your Business with SAS
Companies need to continually adapt to changing business conditions. They can make informed decisions with the help of predictive models, but many businesses still struggle with getting models into production in a reasonable timeframe. This course describes how awareness of ModelOps (model operations) can streamline the journey from preparing data, building models, and deploying them in production environments to managing their performance. Proven DevOps (development operations) disciplines are evolving into the unique practice of deploying models in today’s environment. Use of SAS Container Runtime and SAS Model Risk Management are also addressed, as are pointers about administrating a production environment. This course helps candidates prepare for the SAS Certified ModelOps Specialist exam along with the topics covered in the Managing Models in SAS Viya course. |
1 Beginner | |
|
Managing Models in SAS Viya
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. |
3 Intermediate | |