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 4 modules in this course
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.
This module, you will learn how to set up a Rust development environment, utilize the AWS SDK for Rust, and build AWS Lambda functions with Rust.
Course Structure and Discussion Etiquette•10 minutes
Report a problem with the course•5 minutes
Key Terms•10 minutes
AWS Cloud Adoption Framework for AI•10 minutes
Lesson Reflection•10 minutes
Key Terms•10 minutes
AWS SDK for Rust•10 minutes
Rust by Example•10 minutes
External Lab: AWS SDK S3 Bucket Lister•10 minutes
Launch a Code Editor application in Studio•10 minutes
Tutorial: Get started with Lightsail for Research virtual computers •10 minutes
Lesson Reflection•10 minutes
Key Terms•10 minutes
LLamaIndex•10 minutes
Distroless•10 minutes
Lesson Reflection•10 minutes
4 assignments•Total 420 minutes
Quiz-Getting Started with Developing on AWS for AI•180 minutes
Quiz-Introduction to AWS Cloud Computing for AI•30 minutes
Quiz-Set Up AI Focused Development Environments•30 minutes
Quiz- Developing Serverless Solutions for Data, ML and AI•180 minutes
1 discussion prompt•Total 10 minutes
Meet and Greet (Optional)•10 minutes
2 ungraded labs•Total 120 minutes
Hello Rust Statement•60 minutes
Building and Running Axum Greedy Coin Microservice•60 minutes
AI Pair Programming from CodeWhisperer to Prompt Engineering
Module 2•16 hours to complete
Module details
CodeWhisperer writes code. You learn to guide it. Large language models crunch data, spit out content. Chain-of-thought prompts make models explain themselves. Craft prompts, shape outputs. Build CLI tools, bash functions. Use CodeWhisperer CLI to automate tasks. Fast, efficient coding with AI.
What's included
7 videos10 readings4 assignments2 ungraded labs
Show info about module content
7 videos•Total 32 minutes
Prompt Engineering Workflows•4 minutes
Summarizing Text with Claude•5 minutes
CodeWhisperer for Rust in Cloud9•8 minutes
Install and ConfigureCodeWhisperer CLI•2 minutes
Using CodeWhisperer CLI•4 minutes
Building Bash CLI•3 minutes
Bash Functions•6 minutes
10 readings•Total 100 minutes
Key Terms•10 minutes
Prompt Engineering•10 minutes
Lesson Reflection•10 minutes
Key Terms•10 minutes
CodeWhisperer•10 minutes
LLMs for Coding•10 minutes
Lesson Reflection•10 minutes
Key Terms•10 minutes
CodeWhisperer for the CLI•10 minutes
Lesson Reflection•10 minutes
4 assignments•Total 720 minutes
Quiz-AI Pair Programming from CodeWhisperer to Prompt Engineering•180 minutes
Quiz-Prompt Engineering•180 minutes
Quiz-CodeWhisperer•180 minutes
Quiz-CodeWhisper for the Command-Line•180 minutes
2 ungraded labs•Total 120 minutes
Prompt Engineering with Rust•60 minutes
Hands-on Project•60 minutes
Amazon Bedrock
Module 3•12 hours to complete
Module details
This module, learn Amazon Bedrock capabilities. Apply through model evaluations and customizations.
What's included
6 videos14 readings3 assignments
Show info about module content
6 videos•Total 15 minutes
Key Components of Amazon Bedrock•3 minutes
Exploring the Boto3 Bedrock Client Python SDK•3 minutes
External Lab Challenge: Python List Bedrock Foundation Models•10 minutes
Lesson Reflection•10 minutes
Key Terms•10 minutes
Using Foundation Models•10 minutes
Claude3 Technical Deep Dive for Bedrock•10 minutes
Agents for Amazon Bedrock•10 minutes
Knowledge Base•10 minutes
External Lab: Invoking Foundation Models in Python with Boto3•10 minutes
Lesson Reflection•10 minutes
3 assignments•Total 540 minutes
Quiz-Foundation Models, Knowledge Bases (RAG) and Agents with Bedrock•180 minutes
Quiz-Amazon Bedrock•180 minutes
Quiz- Getting Started with the Bedrock SDK•180 minutes
Project Challenges
Module 4•5 hours to complete
Module details
In this module, you will challenge yourself to apply the concepts covered in the previous module and challenge yourself to apply what you learned in a new context.
What's included
2 videos6 readings1 assignment1 ungraded lab
Show info about module content
2 videos•Total 13 minutes
Introduction to Cargo Lambda•6 minutes
Building Rust Add Function for AWS Lambda•7 minutes
Duke University has about 13,000 undergraduate and graduate students and a world-class faculty helping to expand the frontiers of knowledge. The university has a strong commitment to applying knowledge in service to society, both near its North Carolina campus and around the world.
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 Specialization?
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.
Is financial aid available?
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.