Operationalizing LLMs on Azure
Learn to deploy and manage Large Language Models on Azure with hands-on experience in Semantic Kernel and RAG patterns.
Learn to deploy and manage Large Language Models on Azure with hands-on experience in Semantic Kernel and RAG patterns.
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
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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
Instructors
30 Courses
Pioneering Tech Leader & AI Educator Shaping the Future of Machine Learning
Noah Gift is a distinguished technology leader and founder of Pragmatic AI Labs with a remarkable 30-year career spanning film, TV, telecom, social networks, startups, and big data, currently serving as an Executive-in-Residence at Duke University. As an AWS ML Hero and Python Software Foundation Fellow, he has authored best-selling books through O'Reilly and Pearson on DevOps, MLOps, data engineering, and cloud computing that are widely adopted by major universities. His expertise in MLOps, data engineering, cloud architecture, and Rust programming has led him to teach thousands of students across prestigious institutions including Duke, Northwestern, UC Berkeley, University of San Francisco, and UC Davis, while also collaborating with Caltech and JPL on automated IT systems. Gift's impact extends beyond academia through his work as a startup CTO, his role in developing scalable distributed systems, and his contributions as a keynote speaker at conferences focused on cloud development and ethical AI use, holding certifications from AWS, Google, and Microsoft, and having published over 100 technical works while conducting workshops for organizations like NASA, PayPal, and PyCon.
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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