Operationalizing LLMs on Azure
This course is part of Large Language Model Operations (LLMOps) Specialization.
This comprehensive course equips you with the skills to harness Azure's powerful ecosystem for Large Language Model operations. Designed for data scientists, AI enthusiasts, and cloud professionals, it provides both theoretical knowledge and hands-on experience across four key modules. You'll begin by exploring Azure's AI services, understanding large language models, their benefits, risks, and mitigation strategies. The course then guides you through practical implementation, including managing GPU resources, deploying models through Azure Machine Learning and Azure OpenAI Service, and utilizing inference APIs with Python. You'll master advanced query crafting using Semantic Kernel, implementing functions and plugins, and optimizing LLM interactions. The final module focuses on building robust end-to-end applications using Retrieval Augmented Generation (RAG), Azure AI Search, and GitHub Actions for automated workflows. Whether you're looking to enhance your cloud AI capabilities or build production-ready LLM applications, this course provides the practical knowledge and hands-on experience needed to succeed in the rapidly evolving field of LLMOps.
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What you'll learn
Gain proficiency in leveraging Azure for deploying and managing Large Language Models
Develop advanced query crafting skills using Semantic Kernel
Implement patterns and deploy applications with Retrieval Augmented Generation (RAG)
Use Azure Machine Learning for LLM deployment and inference
Manage GPU quotas and computing resources effectively
Create and configure Azure OpenAI Service resources
Skills you'll gain
This course includes:
4.5 Hours PreRecorded video
4 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This four-module course provides a comprehensive journey into operationalizing Large Language Models (LLMs) on Microsoft Azure. Students begin with an introduction to Azure's AI services, gaining foundational knowledge about LLMs, their capabilities, benefits, and risk mitigation strategies. The second module focuses on practical implementation, teaching students how to leverage Azure Machine Learning and Azure OpenAI Service to deploy models and use inference APIs through Python. In the third module, students master advanced query crafting techniques using Semantic Kernel, learning to optimize LLM interactions through refined prompts and system commands. The final module covers architectural patterns and end-to-end application deployment, focusing on Retrieval Augmented Generation (RAG), Azure AI Search integration, and automated deployment through GitHub Actions. Throughout the course, hands-on labs and assignments reinforce theoretical concepts, preparing students to build robust, production-ready LLM applications.
Introduction to LLMOps with Azure
Module 1 · 3 Hours to complete
LLMs with Azure
Module 2 · 2 Hours to complete
Extending with Functions and Plugins
Module 3 · 2 Hours to complete
Building an End-to-End LLM application in Azure
Module 4 · 2 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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