Large Language Models with Azure
This course is part of Large Language Model Operations (LLMOps).
This practical course teaches you how to harness Azure's AI services for Large Language Model operations. Learn to deploy and manage LLMs, implement advanced query techniques with Semantic Kernel, and build scalable applications using architectural patterns like RAG. The curriculum covers Azure Machine Learning, OpenAI Service integration, GPU optimization, and automated workflows with GitHub Actions. Through hands-on projects, you'll gain expertise in building end-to-end LLM applications while addressing practical concerns like performance, cost-efficiency, and risk mitigation.
Instructors:
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What you'll learn
Deploy and manage Large Language Models using Azure's AI services
Optimize GPU resources for efficient model performance and cost management
Implement advanced query techniques using Semantic Kernel
Develop custom functions and microservices to extend system capabilities
Build end-to-end LLM applications using RAG architecture
Automate testing and deployment workflows with GitHub Actions
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
Shareable certificate
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There are 4 modules in this course
This comprehensive course focuses on mastering Large Language Model operations within the Azure ecosystem. Students learn to leverage Azure's AI services, including Azure Machine Learning and OpenAI Service, for deploying and managing LLMs. The curriculum covers essential topics like GPU quota management, advanced query techniques using Semantic Kernel, and implementation of architectural patterns such as Retrieval Augmented Generation (RAG). Practical skills include building end-to-end LLM applications, automating workflows with GitHub Actions, and optimizing performance while managing costs.
Introduction to LLMOps with Azure
Module 1
LLMs with Azure
Module 2
Extending with Functions and Plugins
Module 3
Building an End-to-End LLM application in Azure
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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