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⇱ AWS Generative AI and Foundation Models | Coursera

AWS Generative AI and Foundation Models

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AWS Generative AI and Foundation Models

This course is part of AI Tooling Specialization

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Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

5 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

5 hours to complete
Flexible schedule
Learn at your own pace

What you'll learn

  • Build RAG pipelines on AWS using Bedrock knowledge bases, embedding pipelines, and foundation models to ground LLM responses in your own data

  • Use Amazon Q Developer for AI-assisted code generation, security scanning, and documentation across VS Code and IntelliJ

  • Compile, quantize, and deploy open-source LLMs using llama.cpp, GGUF format, and AWS GPU instances with performance optimizations from Amdahl's Law

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Recently updated!

April 2026

Assessments

2 assignments

Taught in English

Build your subject-matter expertise

This course is part of the AI Tooling Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 2 modules in this course

Learn to build generative AI solutions on AWS by working hands-on with Amazon Bedrock, Retrieval Augmented Generation pipelines, Amazon Q Developer, and open-source LLM toolchains. You will apply tokenization concepts to understand model pricing and context windows, construct RAG pipelines grounded in your own knowledge bases, and use the Bedrock SDK in Rust and Python to invoke foundation models programmatically. The course covers Amazon Q Developer for AI-assisted code generation, security scanning, and documentation workflows across VS Code and IntelliJ. You will compile llama.cpp with parallel build optimizations informed by Amdahl's Law, package models in the GGUF quantization format, and deploy open-source LLMs on AWS EC2 GPU instances. The course also introduces SageMaker Canvas for no-code visual machine learning and the UV Python packaging tool for dependency management. By completing this course, you will be able to evaluate trade-offs between managed AWS services, open-source toolchains, and no-code platforms for production generative AI workloads.

What's included

19 videos8 readings1 assignment

19 videos•Total 77 minutes
  • Course Intro: Open-Source LLMs•2 minutes
  • Generative AI on AWS•4 minutes
  • What is Tokenization•3 minutes
  • Multiple Model Architecture•4 minutes
  • Intro to RAG•5 minutes
  • RAG on AWS•4 minutes
  • Bedrock Knowledge Agent RAG Demo•3 minutes
  • RAG Bedrock System Walkthrough•3 minutes
  • Bedrock List Rust Demo•3 minutes
  • Bedrock Rust Diagram•3 minutes
  • Amazon Q Developer Intro•3 minutes
  • Developing with Amazon Q Developer•5 minutes
  • Amazon Q Developer IntelliJ•6 minutes
  • Install Amazon Q VS Code•3 minutes
  • Documentation Assistant•7 minutes
  • Amazon Q Code Scanning•4 minutes
  • Bedrock Provisioned IO•4 minutes
  • Setup Bedrock Provisioned IO•5 minutes
  • Evaluate Prompts in Bedrock•6 minutes
8 readings•Total 44 minutes
  • Key Terms•1 minute
  • Reflection•10 minutes
  • Key Terms•1 minute
  • Reflection•10 minutes
  • Key Terms•1 minute
  • Reflection•10 minutes
  • Key Terms•1 minute
  • Reflection•10 minutes
1 assignment•Total 30 minutes
  • AI on AWS•30 minutes

What's included

12 videos9 readings1 assignment

12 videos•Total 51 minutes
  • Amdahl's Law and Compiling llama.cpp•5 minutes
  • llama.cpp Flags Compile•5 minutes
  • GGUF File Format•4 minutes
  • Fixing Python Packaging with UV CLI Tool Demo•4 minutes
  • UV Architecture•2 minutes
  • llama.cpp Toolchain Qwen Coder•5 minutes
  • llama.cpp Qwen2.5 Coder Chatbot Demo•4 minutes
  • llama.cpp on AWS GPU Demo•5 minutes
  • SageMaker Canvas Intro•4 minutes
  • Overview of Canvas UI•3 minutes
  • Working with Dataset•6 minutes
  • Course Conclusion•4 minutes
9 readings•Total 90 minutes
  • Key Terms•10 minutes
  • Reflection•10 minutes
  • Key Terms•10 minutes
  • Reflection•10 minutes
  • Key Terms•10 minutes
  • Reflection•10 minutes
  • Key Terms•10 minutes
  • Reflection•10 minutes
  • Next Steps•10 minutes
1 assignment•Total 15 minutes
  • AWS Generative AI•15 minutes

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Instructors

Pragmatic AI Labs
35 Courses•2,678 learners

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Frequently asked questions

No prior generative AI experience is required. The course starts with foundational concepts like tokenization, foundation models, and RAG architecture before progressing to hands-on implementations with Bedrock, llama.cpp, and SageMaker Canvas.

You will work directly with AWS services throughout the course. Demonstrations include invoking Bedrock models via the Rust SDK, building RAG pipelines with Bedrock knowledge bases, deploying llama.cpp on EC2 GPU instances, and using SageMaker Canvas for no-code ML workflows.

No. The course covers both AWS managed services (Bedrock, SageMaker Canvas, Amazon Q Developer) and open-source toolchains (llama.cpp, GGUF quantization, UV packaging). You will learn to evaluate trade-offs between managed and open-source approaches for different production scenarios.

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.

When you enroll in the course, you get access to all of the courses in the Specialization, 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.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Financial aid available,