GenAI and LLMs on AWS
This course is part of Large Language Model Operations (LLMOps) Specialization.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Large Language Model Operations (LLMOps) Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
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What you'll learn
Deploy and manage LLMs using AWS services
Optimize cost and performance of AI models
Develop with AWS SDK and CodeWhisperer
Implement MLOps best practices
Build secure and scalable AI pipelines
Skills you'll gain
This course includes:
2.25 Hours PreRecorded video
2 quizzes, 10 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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Get a Completion Certificate
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There are 4 modules in this course
This comprehensive course focuses on deploying and managing large language models (LLMs) using AWS services, particularly Amazon Bedrock. Students learn cloud computing fundamentals for AI, development environments setup, and practical MLOps skills. The curriculum covers AWS SDK usage, serverless computing, AI pair programming with CodeWhisperer, and hands-on experience with Amazon Bedrock's foundation models, knowledge bases, and agents.
Getting Started with Developing on AWS for AI
Module 1 · 13 Hours to complete
AI Pair Programming from CodeWhisperer to Prompt Engineering
Module 2 · 16 Hours to complete
Amazon Bedrock
Module 3 · 11 Hours to complete
Project Challenges
Module 4 · 5 Hours to complete
Fee Structure
Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Large Language Model Operations (LLMOps) Specialization
Instructors
Executive in Residence and Founder of Pragmatic AI Labs at Duke University
Noah Gift is the founder of Pragmatic AI Labs and serves as an Executive in Residence at Duke University, where he lectures in the Master of Interdisciplinary Data Science (MIDS) program. He specializes in designing and teaching graduate-level courses on machine learning, MLOps, artificial intelligence, and data science, while also consulting on machine learning and cloud architecture for students and faculty. A recognized expert in the field, Gift is a Python Software Foundation Fellow and an AWS Machine Learning Hero, holding multiple AWS certifications, including AWS Certified Solutions Architect and AWS Certified Machine Learning Specialist. He has authored several influential books, such as Practical MLOps, Python for DevOps, and Pragmatic AI, and has published over 100 technical articles across various platforms, including Forbes and O'Reilly. His extensive industry experience includes roles as CTO and Chief Data Scientist for notable companies like Disney Feature Animation, Sony Imageworks, and AT&T, contributing to major films like Avatar and Spider-Man 3. Gift's work has generated millions in revenue through product development on a global scale. He actively consults startups on machine learning and cloud architecture while leading initiatives to enhance data science education.
Adjunct Assistant Professor at Duke University
Dr. Alfredo Deza is an Adjunct Assistant Professor in the Pratt School of Engineering at Duke University, where he teaches courses on machine learning, programming, and data engineering. He has been involved in academia for several years, focusing on innovative teaching methods and practical applications of technology. Dr. Deza co-authored the book Practical MLOps and has published several other works related to Python and machine learning. His teaching includes courses such as Python Bootcamp and advanced data engineering topics, and he actively develops online courses available on platforms like Coursera. In addition to his academic role, Dr. Deza works in developer relations at Microsoft, leveraging his extensive experience in software engineering and cloud computing to enhance educational content and support for students and faculty. He collaborates with various universities worldwide, including Georgia Tech and Carnegie Mellon University, to promote knowledge sharing in the field of technology and data science.
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