SAS AI for Machine Learning Engineers Subscription

For data scientists, statisticians, modelers, machine learning engineers, and others who use large data and complex analytics to drive mission-critical decisions. The subscription provides the learning and tools you need to effectively manage all aspects of the analytics lifecycle, from data to modeling to deployment.
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Unlock digital courses
Unlimited access to digital courses and books in this subscription.
Prepare for certification
Unlimited access to certification preparation materials.
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Earn a digital badge for each completed course.
Hands-on learning and practice
Get Full access to SAS software to practice as you learn.

About this Subscription



This learning subscription begins with a tour through many applications of machine learning in business and moves into just-enough-statistics to enable you to use machine learning effectively and efficiently. Train and validate a wide variety of machine learning models in SAS. Scale from training a few models at a time to training, monitoring, and updating all the models that drive your business with ModelOps.

Learn how to:
  • Identify the need for machine learning in business.
  • Explain the relevance of statistics in machine learning.
  • Identify important applications of supervised and unsupervised machine learning models.
  • Relate statistics and computer science terminology.
  • Prepare data for predictive analytics.
  • Train, validate and test supervised machine learning models including decision trees, logistic regression, neural networks, gradient boosting, forests, and support vector machines.
  • Evaluate and select the best model based on business needs.
  • Deploy and monitor models with a ModelOps approach.
  • Identify the need for machine learning in business.
  • Explain the relevance of statistics in machine learning.
  • Identify important applications of supervised and unsupervised machine learning models.
  • Relate statistics and computer science terminology.
  • Prepare data for predictive analytics.
  • Train, validate and test supervised machine learning models including decision trees, logistic regression, neural networks, gradient boosting, forests, and support vector machines.
  • Evaluate and select the best model based on business needs.
  • Deploy and monitor models with a ModelOps approach.

Courses Included in Subscription

Applied Statistics for Machine Learning Associate Certification

1

  • COURSE

    Statistics You Need to Know for Machine Learning
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    When it comes to using data, there are two main camps, traditional statistics and machine learning, and the two camps complement each other. Statistics remains highly relevant, irrespective of the "bigness" of data. Its role remains what it has always been, but it is even more important now. There is a need to transition from traditional statistical modeling to the machine learning world. This course introduces the statistical background necessary for machine learning using SAS Viya. Knowledge of statistics relevant to machine learning will prepare you to become a data scientist. The course prepares you for future instruction on doing machine learning (including its underlying methodology that has statistical foundations) and enables you to develop a deeper understanding of machine learning models.

    This course is a prerequisite to many of the courses in the data science curriculum. A more advanced treatment of machine learning occurs in the courses Machine Learning Using SAS Viya, Interactive Machine Learning in SAS Viya, SAS Visual Statistics in SAS Viya: Interactive Model Building, and Supervised Machine Learning Procedures Using SAS Viya in SAS Studio.

    For students interested in statistics for inference and explanatory analysis used in scientific and medical research, Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression is an appropriate foundational course.

  • CERTIFICATION PREP

    SAS Certified Associate: Applied Statistics for Machine Learning
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    As you prepare for the Applied Statistics for Machine Learning certification exam, you will develop a deep understanding of statistical and machine learning concepts as well as learn how to apply explanatory and predictive modeling techniques using SAS Viya.

  • CERTIFICATION PREP

    Practice Exam: Applied Statistics for Machine Learning
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    SAS Certified Specialist: Applied Statistics for Machine Learning

Machine Learning Specialist Certification

2

  • COURSE

    Machine Learning Using SAS® Viya®
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    This course discusses the theoretical foundation for techniques associated with supervised machine learning models. A series of demonstrations and practices is used to reinforce all the concepts and the analytical approach to solving business problems. 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, by illustrating data exploration, data preprocessing, feature selection, model training and validation, model assessment, and scoring. This course is the core of the SAS Viya Data Mining and Machine Learning curriculum. It 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.

  • CERTIFICATION PREP

    SAS Certified Specialist: Machine Learning Using SAS Viya
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    In preparation for the SAS Viya Supervised Machine Learning Pipelines certification exam, you will learn use SAS Visual Data Mining and Machine Learning software to prepare data and feature engineering; create supervised machine learning models; assess; model performance; and deploy models into production.

  • CERTIFICATION PREP

    Practice Exam: SAS Viya Supervised Machine Learning Pipelines
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    This Certification is for data scientists who create supervised machine learning models using pipelines in SAS Viya. Successful candidates should be familiar with SAS Visual Data Mining and Machine Learning software and be skilled in tasks such as:

    • Preparing data and feature engineering
    • Creating supervised machine learning models
    • Assessing model performance
    • Deploying models into production

    Enroll


SAS ModelOps Specialist Certification

3

  • COURSE

    Managing Models in SAS® Viya®
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    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.

  • COURSE

    ModelOps: Governing AI and Machine Learning Models That Drive Your Business with SAS®
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    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.

  • CERTIFICATION PREP

    SAS Certified ModelOps Specialist
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    In preparation for this exam, you will learn how to define the ModelOps framework, develop model proposals, develop and manage models, and deploy models. You will also learn how to execute multiple production steps including preprocessing, scoring, post-processing, business actions, and performance monitoring.

  • CERTIFICATION PREP

    Practice Exam: Managing the Model Life Cycle Using ModelOps
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    Practice Exam: Managing the Model Life Cycle Using ModelOps

Speciality Topics

4

  • COURSE

    The Modeling Life Cycle for Data Scientists
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    This course gives an overview of the statistical methods used by data scientists, with an emphasis on the applicability to business problems. Attendees do not need access to software for this course, and the mathematical details are kept to a minimum.

  • COURSE

    Network Analysis and Network Optimization in SAS® Viya®
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    This course provides a set of network analysis and network optimization solutions using the NETWORK and OPTNETWORK procedures in SAS Viya. Real-world applications are emphasized for each algorithm introduced in this course, including using network analysis as a stand-alone unsupervised learning technique, as well as incorporating network analysis and optimization to augment supervised learning techniques to improve machine learning model performance through input/feature creation.

  • COURSE

    Supervised Machine Learning Procedures Using SAS® Viya® in SAS® Studio
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    This course covers a variety of machine learning techniques that are performed in a scalable and in-memory execution environment. The course provides hands-on experience with SAS Visual Data Mining and Machine Learning through SAS Studio, a user interface for SAS programming. The machine learning techniques include logistic regression, decision tree and ensemble of trees (forest and gradient boosting), neural networks, support vector machine, factorization machine, and Bayesian networks.

  • COURSE

    Interactive Machine Learning in SAS® Viya®
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    This course provides a theoretical foundation for using machine learning capabilities in SAS Viya, as well as hands-on experience using the tool through the SAS Visual Analytics interface. The course uses an interactive approach to teach you visualization, model assessment, and model deployment while introducing you to a variety of machine learning techniques.

    The SAS Viya 3.5 e-learning version of this course uses the title SAS Visual Data Mining and Machine Learning in SAS Viya: Interactive Machine Learning.

  • COURSE

    Using Python and R with SAS® Viya® for Advanced Analytics
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    In this course, you learn to use R and Python to take control of the SAS Viya Cloud Analytic Services (CAS) distributed computing environment to develop machine learning models. You learn to upload data into the in-memory distributed environment, analyze data using Pandas like functionality, build machine learning models, and assess those models in CAS using familiar open-source functionality via the SWAT (SAS Scripting Wrapper for Analytics Transfer) package.

AI Literacy

5

  • COURSE

    Data Literacy Essentials
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    This course is for anyone interested in learning how to use data in meaningful ways. It is designed as an introductory course to data literacy, starting with the basics: what is data, what does it mean to be data literate, and why is it important in today’s world?

    This self-paced course follows the journeys of a concerned parent, a small business owner, and a public health expert, all of whom rely on data to navigate the COVID-19 pandemic. It connects skills we already use to strategies for engaging with data in more intentional and meaningful ways.

    Whether you are an advanced high school student, a K-12 educator, a post-secondary student or educator, or an independent learner hoping to reskill or upskill, this course is for you.

  • COURSE

    Data Literacy in Practice
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    In this self-paced course, you will discover practical skills to explore and visualize data. You will follow a small business owner's data-driven journey to improve company performance. Using this real-world context, you'll connect knowledge to strategies you can act on. The course is for everyone, no matter where you are on your data literacy journey. By focusing on conceptual and practical understanding rather than distracting mathematical jargon, learners feel supported and encouraged throughout the process. Whether you are a high school student, a K-12 educator, a post-secondary student or educator, or an independent learner hoping to reskill or upskill, this course is for you.

  • COURSE

    Generative AI Using SAS®
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    Generative Artificial Intelligence (GenAI) is a rapidly developing area of machine learning, with application across business, government, and academia. In this course, you will learn about different types of GenAI and see examples of how SAS can enhance your efforts to make the most of these techniques.

    This course will be released several lessons at a time until all lessons are available. We expect that each lesson can be completed in about an hour, and you can work at your own pace to complete the material. As we release new lessons, you might lose progress through the material that you have completed, so please make a note of where you are leaving off before exiting the course.

  • COURSE

    Responsible Innovation and Trustworthy AI
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    This course is designed for anyone who wants to gain a deeper understanding about the importance of trust and responsibility in AI, analytics, and innovation. The content is especially geared to those who are making business decisions based on machine learning and AI systems and those who are designing and training AI systems.

    Whether you are a programmer, an executive, an advisory board member, a tester, a manager, or an individual contributor, this course helps you gain foundational knowledge and skills to consider the issues related to responsible innovation and trustworthy AI. Empowered with the knowledge from this course, you can strive to find ways to design, develop, and use machine learning and AI systems more responsibly.

    This course will be released several modules at a time until all modules are available. We expect that each module can be completed in under an hour, and you can work at your own pace to complete the material. As we release new modules, you might lose progress through the material that you have completed, so please make a note of where you are leaving off before exiting the course.

SAS Products Covered

  • SAS Viya
  • SAS Model Manager
  • SAS Machine Learning


Digital Badges

Earn a digital badge for each course that you complete and for each credential that you earn. Show off your achievements on your resume and in your social channels to highlight your skills and connect with potential employers.

Certification Preparation



When you complete the courses in this subscription, you will have the demonstrated skills necessary to prepare you to earn the following credentials.

  • SAS Certified Associate: Applied Statistics for Machine Learning
  • SAS Certified Specialist: Machine Learning Using SAS Viya
  • SAS Certified ModelOps Specialist

    • “I think the self-paced training was the BEST I’ve ever taken. The videos were short and segmented correctly to keep my attention and the activities and quizzes were just enough to help my confidence.”

    Tony Mayo, SAS Customer