Introduction to Generative AI - Pragmatic AI Labs
This course is part of Large Language Model Operations (LLMOps).
This comprehensive beginner-friendly course provides a solid foundation in generative AI technology. Students learn how generative AI works through interactive lessons and practical exercises, mastering effective prompting techniques and understanding model capabilities and limitations. The curriculum covers major generative models, prompt engineering fundamentals, and system building techniques like Retrieval Augmented Generation. Through hands-on labs and real-world examples, participants gain practical experience with both open-source models and cloud APIs, preparing them to navigate and utilize generative AI technologies confidently.
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What you'll learn
Understand generative AI fundamentals and model architectures
Master prompt engineering techniques for effective model interaction
Explore major foundation models and their capabilities
Build robust generative AI applications using modern tools
Deploy AI solutions on cloud platforms
Skills you'll gain
This course includes:
PreRecorded video
Quizzes, Labs, Discussions
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 3 modules in this course
The course explores fundamental concepts of generative AI, including model architectures, training processes, and practical applications. Students learn prompt engineering techniques, system building approaches, and deployment strategies through hands-on experience with leading AI platforms and tools.
Foundations of Generative AI
Module 1
Interacting with Models
Module 2
Building Robust Generative AI Systems
Module 3
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)
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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