OpenCourser brand icon
Sign in
We may earn an affiliate commission when you visit our partners.
Course image
Course image
Coursera logo

GenAI and LLMs on AWS

Noah Gift, Alfredo Deza, and Derek Wales

This course will teach you how to deploy and manage large language models (LLMs) in production using AWS services like Amazon Bedrock. By the end of the course, you will know how to:

Choose the right LLM architecture and model for your application using services.

Optimize cost, performance and scalability of LLMs on AWS using auto-scaling groups, spot instances and container orchestration

Monitor and log metrics from your LLM to detect issues and continuously improve quality

Build reliable and secure pipelines to train, deploy and update models using AWS services

Read more

This course will teach you how to deploy and manage large language models (LLMs) in production using AWS services like Amazon Bedrock. By the end of the course, you will know how to:

Choose the right LLM architecture and model for your application using services.

Optimize cost, performance and scalability of LLMs on AWS using auto-scaling groups, spot instances and container orchestration

Monitor and log metrics from your LLM to detect issues and continuously improve quality

Build reliable and secure pipelines to train, deploy and update models using AWS services

Comply with regulations when deploying LLMs in production through techniques like differential privacy and controlled rollouts

This course is unique in its focus on real-world operationalization of large language models using AWS. You will work through hands-on labs to put concepts into practice as you learn. Whether you are a machine learning engineer, data scientist or technical leader, you will gain practical skills to run LLMs in production.

Enroll now

Here's a deal for you

Save money when you learn with a deal that may be relevant to this course.
All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.
Valid until March 12, 2027
Save 40% Off 3 Months
Access thousands of top-rated learning programs with a Coursera Plus monthly subscription and take 40% off 3 months for a limited time.
Take
40%
off
Get offer

What's inside

Syllabus

Getting Started with Developing on AWS for AI
This week, you will learn how to set up a Rust development environment, utilize the AWS SDK for Rust, and build AWS Lambda functions with Rust.
Read more

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Teaches skills and knowledge that are highly relevant to industry
Develops advanced professional skills in deploying and managing large language models
Taught by Noah Gift, Alfredo Deza, and Derek Wales, who are recognized for their work in large language models
Covers topics that are unique to the industry
Offers hands-on labs and interactive materials to enhance learning
Requires learners to come in with ample background knowledge and experience

Save this course

Create your own learning path. Save this course to your list so you can find it easily later.
Save

Reviews summary

Practical llm deployment on aws for professionals

According to learners, this course offers a highly practical approach to operationalizing LLMs on AWS, providing essential skills for real-world production deployments. Students praise its focus on key AWS services like Amazon Bedrock, enabling them to optimize performance, scalability, and security. The curriculum, designed for machine learning engineers, data scientists, and technical leaders, includes valuable hands-on labs that effectively bridge theoretical concepts with practical application. While the introduction to Rust development and CodeWhisperer for prompt engineering is unique, it aligns with automating tasks for efficient GenAI workflows.
Covers Rust and CodeWhisperer for efficiency.
"The introduction to Rust for AWS Lambda functions was a valuable addition."
"Learning prompt engineering with CodeWhisperer made me more efficient."
"The course provides a good overview of AI pair programming concepts."
Highly valuable for ML engineers and data scientists.
"As a machine learning engineer, I found the course highly relevant to my career."
"It provides practical skills for data scientists needing to deploy models."
"I appreciated the focus on building reliable and secure production pipelines."
Deep dive into key AWS services for LLMs.
"I learned Bedrock capabilities and how to apply them through model evaluations."
"The course explained optimizing cost and scalability of LLMs on AWS."
"It showed me how to monitor and log metrics from my LLM deployments on AWS."
Focuses on practical deployment of LLMs on AWS.
"I gained practical skills to run LLMs in production effectively."
"The hands-on labs helped me put concepts into practice immediately."
"This course is unique in its focus on real-world operationalization of LLMs."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in GenAI and LLMs on AWS with these activities:
Read Deep Learning with Python
Strengthen your understanding of deep learning concepts and techniques with this recommended reading
View Deep Learning with Python, Second Edition on Amazon
Show steps
  • Read the book's introduction and first chapter
  • Complete the exercises in the book's first chapter
  • Summarize the key concepts covered in the book's first chapter
Review Rust
Reinforce your foundational understanding of Rust to better prepare for this course's material
Browse courses on Rust
Show steps
  • Review the Rust documentation
  • Complete the Rustlings tutorial
  • Build a simple Rust project
Review AWS Lambda functions
Strengthen your foundational knowledge of AWS Lambda functions to better prepare for this course's material
Browse courses on AWS Lambda
Show steps
  • Review the AWS Lambda documentation
  • Complete the AWS Lambda tutorial
  • Build a simple AWS Lambda function
Four other activities
Expand to see all activities and additional details
Show all seven activities
Follow Amazon Bedrock tutorials
Enhance your understanding of Amazon Bedrock through guided tutorials
Browse courses on Amazon Bedrock
Show steps
  • Complete the Amazon Bedrock getting started tutorial
  • Complete the Amazon Bedrock advanced tutorial
  • Build a simple Amazon Bedrock application
Attend an LLM workshop
Enhance your knowledge and skills in LLM deployment and management through a workshop
Browse courses on Large Language Models
Show steps
  • Identify and register for an LLM workshop
  • Attend the workshop and actively participate in the activities
  • Implement what you learned from the workshop in your own projects
Practice deploying and managing LLMs
Develop your practical skills in deploying and managing LLMs
Browse courses on Large Language Models
Show steps
  • Deploy a simple LLM using Amazon Bedrock
  • Manage the LLM's resources and performance
  • Monitor the LLM's usage and identify areas for improvement
Write a blog post on LLM deployment
Solidify your understanding of LLM deployment by sharing your knowledge through writing
Show steps
  • Choose a specific aspect of LLM deployment to focus on
  • Research the topic and gather relevant information
  • Write a clear and concise blog post that explains the topic

Career center

Learners who complete GenAI and LLMs on AWS will develop knowledge and skills that may be useful to these careers:
AI Engineer
The GenAI and LLMs on AWS course develops foundational capabilities for AI Engineers. It goes into detail on the operationalization of large language models using AWS. The deployment and management skills are invaluable to the efficient scaling of AI projects.
See salaries and explore the career path for AI Engineer
Natural Language Processing Engineer
The GenAI and LLMs on AWS course is highly relevant to Natural Language Processing Engineers as it delves into the practicalities of deploying and managing large language models on AWS, essential skills for this specialized role.
See salaries and explore the career path for Natural Language Processing Engineer
Site Reliability Engineer
The GenAI and LLMs on AWS course is an excellent choice for Site Reliability Engineers looking to deepen their understanding of deploying and managing AI models in production. The focus on reliability, scalability, and monitoring will be especially beneficial.
See salaries and explore the career path for Site Reliability Engineer
AI Researcher
The GenAI and LLMs on AWS course provides AI Researchers with a practical understanding of deploying and managing large language models on AWS, which can support their research and innovation in the field.
See salaries and explore the career path for AI Researcher
Cloud Architect
GenAI and LLMs on AWS equips learners with the knowledge necessary to make informed decisions about the deployment of AI and ML models in AWS environments. This course can help Cloud Architects specializing in ML gain expertise in optimizing and scaling LLM deployments on AWS.
See salaries and explore the career path for Cloud Architect
Machine Learning Scientist
The GenAI and LLMs on AWS course can empower Machine Learning Scientists with the knowledge and expertise to deploy and manage large language models on AWS, a valuable addition to their skillset.
See salaries and explore the career path for Machine Learning Scientist
Machine Learning Engineer
Machine Learning Engineers are responsible for the development and deployment of AI models. By taking this course, individuals will learn best practices, techniques, and tools for deploying and managing large language models on AWS.
See salaries and explore the career path for Machine Learning Engineer
Data Engineer
The GenAI and LLMs on AWS course is designed to provide Data Engineers interested in leveraging large language models with the tools and practices to successfully build and manage these models on AWS.
See salaries and explore the career path for Data Engineer
Data Science Manager
The GenAI and LLMs on AWS course provides Data Science Managers with crucial insights into the deployment and management of large language models on AWS, enabling them to effectively lead and manage teams in this domain.
See salaries and explore the career path for Data Science Manager
DevOps Engineer
The GenAI and LLMs on AWS course provides DevOps Engineers with valuable knowledge for integrating AI and ML models into their continuous integration and deployment pipelines.
See salaries and explore the career path for DevOps Engineer
Data Scientist
Data Scientists play a key role in the development and deployment of AI models. The GenAI and LLMs on AWS course provides a strong foundation for Data Scientists to apply their knowledge to large language models.
See salaries and explore the career path for Data Scientist
Data Analyst
The GenAI and LLMs on AWS course can provide Data Analysts with a solid foundation in deploying and managing large language models on AWS, which can enhance their capabilities in data analysis and modeling.
See salaries and explore the career path for Data Analyst
Software Engineer
The GenAI and LLMs on AWS course will help Software Engineers gain expertise in deploying and managing large language models on AWS. This knowledge can be applied to various industries and projects.
See salaries and explore the career path for Software Engineer
Product Manager
Product Managers involved in AI or ML products may find the GenAI and LLMs on AWS course beneficial in understanding the technical aspects of deploying and managing large language models on AWS.
See salaries and explore the career path for Product Manager
Business Analyst
Business Analysts may find the GenAI and LLMs on AWS course useful for understanding the potential of large language models and how they can be applied to business problems.
See salaries and explore the career path for Business Analyst

Reading list

We've selected 13 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in GenAI and LLMs on AWS.
Cover image
Neural Network Methods for Natural Language...
Save
Provides a comprehensive introduction to recurrent neural networks (RNNs) for natural language processing (NLP).
Neural Network Methods for Natural Language...
Paperback
$$
Cover image
Deep Learning with Python, Second Edition
Save
Provides a comprehensive overview of deep learning with Python. It covers topics such as the history of deep learning, different types of deep learning models, and applications of deep learning.
Deep Learning with Python, Second Edition
Paperback
$$
Deep Learning with Python, Second Edition
Kindle Edition
$$
Cover image
Hands-On Machine Learning with Scikit-Learn, Keras,...
Save
Provides a hands-on introduction to machine learning with Scikit-Learn, Keras, and TensorFlow. It covers topics such as data preparation, model training, and deployment.
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Kindle Edition
$$
Cover image
The Data Science Handbook
Save
Provides a comprehensive overview of data science. It covers topics such as data collection, data analysis, and data visualization.
The Data Science Handbook: Advice and Insights from...
Paperback
$$
Cover image
The Elements of Statistical Learning
Save
Provides a comprehensive overview of statistical learning. It covers topics such as data analysis, model selection, and prediction.
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Kindle Edition
$$$
Cover image
An Introduction to Statistical Learning
Save
Provides a less technical introduction to statistical learning. It covers topics such as data analysis, model selection, and prediction.
An Introduction to Statistical Learning: with...
Hardcover
$$$
Cover image
Probabilistic Machine Learning
Save
Provides a probabilistic perspective on machine learning. It covers topics such as Bayes' theorem, graphical models, and Markov chain Monte Carlo.
Probabilistic Machine Learning: Advanced Topics...
Hardcover
$$$$
Gaussian Processes for Machine Learning
Save
Provides a comprehensive overview of Gaussian processes for machine learning. It covers topics such as the history of Gaussian processes, different types of Gaussian processes.
Gaussian Processes for Machine Learning (Adaptive...
Hardcover
$$$
Bayesian Reasoning and Machine Learning South Asian...
Save
Provides a comprehensive overview of Bayesian reasoning and machine learning. It covers topics such as the history of Bayesian reasoning, different types of Bayesian models, and applications of Bayesian reasoning.
Bayesian Reasoning and Machine Learning Paperback
Paperback
$$$
Cover image
Machine Learning for Hackers
Save
Provides a hands-on introduction to machine learning for hackers. It covers topics such as data collection, data analysis, and model training.
Machine Learning for Hackers: Case Studies and...
Paperback
$$
Machine Learning for Hackers: Case Studies and...
Kindle Edition
$$
Cover image
Data Mining
Save
Provides a comprehensive overview of data mining. It covers topics such as data collection, data analysis, and model training.
Data Mining: Practical Machine Learning Tools and...
Paperback
$$$
DATA MINING, 4TH EDITION
Paperback
$$
Data Mining: Practical Machine Learning Tools and...
Kindle Edition
$$
Cover image
Machine Learning in Action
Save
Provides a hands-on introduction to machine learning. It covers topics such as data collection, data analysis, and model training.
Machine Learning in Action
Paperback
$$
(中文) 机器学习实战 (图灵程序设计丛书 72) (Chinese Edition)
Kindle Edition
$
Cover image
Deep Learning
Save
Provides a comprehensive overview of deep learning. It covers topics such as the history of deep learning, different types of deep learning models, and applications of deep learning.
Deep Learning
Paperback
$$$
Deep Learning (Adaptive Computation and Machine...
Hardcover
$$$
Deep Learning (Adaptive Computation and Machine...
Kindle Edition
$$$

Share

Help others find this course page by sharing it with your friends and followers:
Facebook
LinkedIn
Reddit
X
Link
Email

Similar courses

Here are nine courses similar to GenAI and LLMs on AWS.
Cloud Computing Technologies - Bachelor's
Most relevant
Getting Started with Amazon RDS for MariaDB
Most relevant
Amazon RDS Service Primer
Most relevant
AWS Advanced
Most relevant
Cloud and AWS Basics
Most relevant
Fundamentals of Analytics on AWS
Most relevant
AWS: CI/CD Pipelines and Deployment Strategies
Most relevant
Generative AI for Data Science
Most relevant
Introduction to On-Device AI
Most relevant

Coursera Buyer Protection & Guarantee

Verified partner policies & purchase protections

Accredited University Partner
Free Trial & Audit

7-Day Free Trial & Free Audit

7-day free trial on monthly Coursera Plus subscriptions; free audit option available on most individual courses to explore video lectures and readings at no charge.

Return & Refund Policy

14-Day Refund Guarantee

14-day refund window on annual Coursera Plus subscriptions and single-course purchases prior to certificate issuance. Monthly subscriptions do not offer refunds once billed.

Buyer Protections

Verified Academic & Industry Partner

Accredited university and industry certifications (e.g. Stanford, Google, IBM, Yale), secure SSL payment processing, and verifiable digital certificate links.

Key Purchase Protections
14-day refund window on annual Coursera Plus and single courses
7-day free trial on monthly Coursera Plus subscriptions
Free course audit option for lectures and readings
Verified certificates from top universities & companies
OpenCourser Verified Partner Policy details last reviewed: 2026-10-22
Course image
Effort
4 weeks of study, 5 hours/week
Level
Introductory
Via
Coursera
Institution
Duke University
Instructors
Noah Gift
Alfredo Deza
Derek Wales
Language
English
This course belongs to a collection called Large Language Model Operations (LLMOps). It's comprised of five courses:
  1. Databricks to Local LLMs
  2. Open Source LLMOps Solutions
  3. Operationalizing LLMs on Azure
  4. GenAI and LLMs on AWS (viewing)
  5. Advanced Data Engineering
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Teaches skills and knowledge that are highly relevant to industry
Develops advanced professional skills in deploying and managing large language models
Taught by Noah Gift, Alfredo Deza, and Derek Wales, who are recognized for their work in large language models
Covers topics that are unique to the industry
Offers hands-on labs and interactive materials to enhance learning
Requires learners to come in with ample background knowledge and experience
Share this
Share to help others discover this course.
Facebook LinkedIn X Reddit Link Email
Begin learning today
Enroll now to gain full access to GenAI and LLMs on AWS.
Enroll now
Save for later
Add this course to your list. Find it anytime.
Save
Our mission

OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.

Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.

Find this site helpful? Tell a friend about us.

Affiliate disclosure

We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.

Your purchases help us maintain our catalog and keep our servers humming without ads.

Thank you for supporting OpenCourser.

© 2016 - 2025 OpenCourser