Open Source LLM Operations
This course is part of Large Language Model Operations (LLMOps).
This comprehensive course dives deep into open source Large Language Models (LLMs), focusing on practical implementation and operations. Students will explore cutting-edge architectures like Transformers, gain hands-on experience with model fine-tuning using SkyPilot, and master efficient deployment using LoRAX and vLLM. The curriculum covers running pre-trained models, understanding LLM architectures, and implementing advanced deployment strategies, all while using state-of-the-art open source tools.
Instructors:
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What you'll learn
Deploy and run local large language models effectively
Master model fine-tuning techniques with SkyPilot
Implement efficient model serving using LoRAX and vLLM
Develop practical solutions using open source AI tools
Understand and work with advanced LLM architectures
Create scalable training workflows across cloud providers
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 4 modules in this course
This intensive course provides a thorough exploration of open source Large Language Models and their practical applications. Starting with fundamental concepts, students learn to access and utilize pre-trained models through libraries like HuggingFace Transformers. The curriculum progresses through local LLM deployment, model fine-tuning, and advanced topics like GPU-accelerated MLOps workflows. Special emphasis is placed on hands-on experience with tools like LLamaFile, Whisper.cpp, and SkyPilot for building and deploying AI solutions.
Getting Started with Open Source Ecosystem
Module 1
Using Local LLMs from LLamaFile to Whisper.cpp
Module 2
Applied Projects
Module 3
Recap and Final Challenges
Module 4
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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