Free
Course

Generative AI on AWS: Building GenAI Models with Amazon SageMaker

Build cutting-edge generative AI models with Amazon SageMaker. Explore GANs, VAEs, optimization, transfer learning, deployment, and monitoring techniques.

  • Skill level

    Intermediate
  • Time to complete

    Approx. 2 hours
  • Certificate of completion

    Yes

About this course

This comprehensive course introduces learners to the world of generative artificial intelligence (AI) models within machine learning (ML), focusing on Amazon SageMaker as a prime tool for design, training, optimization, deployment, and monitoring. In this course, we will begin with a deep dive into the fundamental concepts and types of generative models like generative adversarial networks (GANs) and variational autoencoders (VAEs). We will explore their pros, cons, and relevant architectures. Next, we will use hands-on tutorials and in-depth discussions to guide you through the steps of designing a GAN architecture, leveraging SageMaker's built-in algorithms, preprocessing data, and distributed training capabilities. We will then delve into optimization techniques, transfer learning, and quality evaluation methods, looking at ways apply these concepts in real-world scenarios. Lastly, we will introduce deployment strategies in SageMaker, highlighting how to serve models as endpoints for real-time inferences and how to efficiently monitor and troubleshoot their generative models.

Learning objectives

  • Discover the key concepts covered in this course
  • Outline the fundamental concepts of generative models and their applications in machine learning
  • Analyze the different types of generative models, including generative adversarial networks (gans) and variational autoencoders (vaes)

How it works

Expert-led videos

In this course, you'll watch videos created by industry-leading experts for some of the biggest tech companies in the world. They'll cover key concepts, go through sample applications, prepare you for industry certifications, and more. Watch on any device — whenever and wherever you want — to learn at your own pace.

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The Relationship Between the Cloud & AI Explained

Cory Stieg
May 1, 2024

As AI finds its way into so many aspects of our lives, it’s even more important to understand the infrastructure that powers this valuable tool. That includes the cloud, which is a big part of how AI apps are deployed and scaled.  

Cloud computing basically refers to internet-delivered services like servers, storage, databases, networks, software, and analytics. The cloud provides computational power and ample data storage that’s needed to train, develop, and deploy cutting-edge AI models.  

If you’re looking for a way to get started with cloud technology, we recently launched a collection of free cloud courses. These courses provide you with foundational knowledge in core cloud providers including Microsoft Azure, Amazon Web Services, Google Cloud, and CompTIA Cloud Essentials+. With these cloud courses, you have the option to dive in and explore for free before committing to a cloud certification program. They’re also an excellent way to complement the in-demand AI and coding skills that you’re already learning. 

Ahead, we’ll break down the relationship between cloud computing and AI with insight from Michael Shannon, a Senior Analyst in Tech and Dev at Skillsoft, who developed the curriculum for many of our cloud courses.  

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