End to End LLMs with Azure
Master Azure for LLM applications: Deploy models, implement RAG, use Azure AI Search, and automate with GitHub Actions for end-to-end LLM solutions.
Master Azure for LLM applications: Deploy models, implement RAG, use Azure AI Search, and automate with GitHub Actions for end-to-end LLM solutions.
This comprehensive course equips learners with skills to leverage Azure for building and deploying Large Language Model (LLM) applications. Students will learn to use Azure OpenAI Service for deploying LLMs, utilizing inference APIs, and integrating with Python. The course covers architectural patterns like Retrieval-Augmented Generation (RAG) and Azure services such as Azure AI Search for robust applications. Participants will gain insights into streamlining deployments with GitHub Actions and apply their knowledge by implementing RAG with Azure Search, creating GitHub Actions workflows, and deploying end-to-end LLM applications.
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What you'll learn
Create Large Language Model endpoints in Azure
Use GitHub Actions to deploy containerized applications for LLMs
Implement Retrieval-Augmented Generation (RAG) with Azure AI Search
Develop Python applications integrating Azure OpenAI Service
Understand and apply LLM architectural patterns in Azure
Manage Azure OpenAI Service resources, keys, and quotas
Skills you'll gain
This course includes:
67 Minutes PreRecorded video
2 assignments
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There is 1 module in this course
This course provides a comprehensive introduction to building and deploying Large Language Model (LLM) applications using Azure. Students will learn to leverage Azure OpenAI Service for LLM deployment, understand and implement architectural patterns like Retrieval-Augmented Generation (RAG), and utilize Azure AI Search for robust application development. The course also covers automation and deployment strategies using GitHub Actions. Through hands-on exercises and projects, learners will gain practical experience in creating end-to-end LLM solutions, from model deployment to application integration and automated deployment pipelines.
Get Started with LLMs in Azure
Module 1 · 9 Hours to complete
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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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