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
In this module, you meet the instructor and learn about course logistics, such as how to access the software for this course.
涵盖的内容
8个视频1个应用程序项目
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8个视频•总计15分钟
Course Overview•2分钟
Overview•0分钟
The Analytical Life Cycle•3分钟
Key User Roles•3分钟
Managed Model Life Cycle•1分钟
Development Operations Pipeline•1分钟
Model Operations•1分钟
Model Operations Environments•3分钟
1个应用程序项目•总计60分钟
Access SAS Viya for Learners•60分钟
Working with Projects and Models
第 2 单元•小时 后完成
单元详情
In this module, you learn about working with projects and models.
涵盖的内容
22个视频14篇阅读材料1个作业
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22个视频•总计68分钟
Overview•0分钟
Analytics Life Cycle•1分钟
Model Manager Services•1分钟
A Modeling Process•1分钟
Demo: Modeling Life Cycle•3分钟
Demo: Accessing the Data Files•4分钟
Demo: Creating a New Project•7分钟
Demo: Quickstart Wiz•1分钟
Demo: Importing Models into a Project•4分钟
Demo: Setting Model Properties•8分钟
Python Model Coding Considerations•1分钟
Deploy a Model•2分钟
Help with Python Models•0分钟
Demo: Importing a Python Model•6分钟
Working with R Models•1分钟
Demo: Importing an Open Source R Model into Model Manager•4分钟
Demo: Comparing Models•5分钟
Demo: Testing a Model•5分钟
Demo: Setting a Champion Model•3分钟
Demo: Adding a Workflow Definition•5分钟
Demo: Starting a Workflow•1分钟
Demo: Completing Workflow Tasks•4分钟
14篇阅读材料•总计140分钟
Resource•10分钟
General Project Properties•10分钟
Types of Model Functions•10分钟
Practice: Creating a New Project•10分钟
Practice: Importing a Data Source and Profiling the Data•10分钟
Resource•10分钟
General Model Properties•10分钟
Demo Steps: Using a notebook to build Python models, add and test score code in a new Model Manager project•10分钟
Resource•10分钟
Working with R Models•10分钟
The R SASCTL Package•10分钟
Practice: Importing Models from SAS Package File, PMML, and ZIP Formats•10分钟
Self-Study Demo: Enabling a Workflow•10分钟
Resource•10分钟
1个作业•总计30分钟
Question: Model Manager•30分钟
Model Deployment
第 3 单元•小时 后完成
单元详情
In this module, you learn about model deployment.
涵盖的内容
17个视频3篇阅读材料1个作业
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17个视频•总计61分钟
Overview•1分钟
Operationalizing Analytics•2分钟
Publishing Models•3分钟
Demo: Publishing a Champion Model•4分钟
Demo: Adding and Testing a CAS Publishing Destination (Part 1)•4分钟
Demo: Adding and Testing a CAS Publishing Destination (Part 2)•3分钟
Deployment Considerations•1分钟
Practical Deployment Considerations•1分钟
Scoring Output Table Considerations•1分钟
Demo: Model Deployment in CAS•8分钟
Monitoring Model Performance•1分钟
Performance Data Source Choices•1分钟
Available Performance Metrics•4分钟
Demo: Running Performance Jobs•12分钟
Demo: Model Cards•4分钟
Automating Model Performance Reporting•2分钟
Demo: Scheduling a Performance Job•10分钟
3篇阅读材料•总计30分钟
Additional Ways to Create a CASLIB for a Publishing Destination•10分钟
Resource•10分钟
Model Retraining•10分钟
1个作业•总计30分钟
Question: Score Code•30分钟
Additional Topics (self-study)
第 4 单元•小时 后完成
单元详情
In this self-study module, you learn about scoring visual text analytics models.
涵盖的内容
1个视频8篇阅读材料
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1个视频
Overview•0分钟
8篇阅读材料•总计80分钟
Scoring Visual Text Analytics Models•10分钟
Model Repositories•10分钟
How to Fit a Scoring Script for Model Containerization•10分钟
Through innovative software and services, SAS empowers and inspires customers around the world to transform data into intelligence. SAS is a trusted analytics powerhouse for organizations seeking immediate value from their data. A deep bench of analytics solutions and broad industry knowledge keep our customers coming back and feeling confident. With SAS®, you can discover insights from your data and make sense of it all. Identify what’s working and fix what isn’t. Make more intelligent decisions. And drive relevant change.
When will I have access to the lectures and assignments?
To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Certificate?
When you enroll in the course, you get access to all of the courses in the Certificate, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.