End to End LLM with Azure
This course is part of Generative AI Fundamentals.
This comprehensive course teaches you how to leverage Azure's ecosystem for building and deploying Large Language Model applications. You'll master Azure OpenAI Service integration with Python, explore advanced architectural patterns like Retrieval-Augmented Generation (RAG), and learn to enhance LLM capabilities using Azure Search. The course covers deployment automation with GitHub Actions and provides hands-on experience in implementing end-to-end LLM solutions. Through practical exercises and real-world scenarios, you'll develop the skills needed to create robust LLM applications in production environments.
Instructors:
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What you'll learn
Deploy and integrate Large Language Models using Azure OpenAI Service
Implement Retrieval-Augmented Generation patterns with Azure Search
Automate testing and deployment workflows with GitHub Actions
Build production-ready LLM applications on Azure
Integrate Azure OpenAI APIs with Python applications
Skills you'll gain
This course includes:
8 Hours PreRecorded video
2 assignments, 3 lab exercises
Access on Mobile, Tablet, Desktop
Limited Access access
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There is 1 module in this course
The course offers a thorough introduction to building end-to-end Large Language Model applications using Azure services. Students learn practical skills in deploying LLMs with Azure OpenAI Service, implementing advanced architectural patterns like RAG, and automating deployments using GitHub Actions. The curriculum emphasizes hands-on experience through labs and assignments, covering everything from basic API integration to complex application architectures. Special focus is placed on real-world implementation scenarios and best practices for production deployments.
LLMs with Azure OpenAI Service
Module 1
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: Generative AI Fundamentals
Instructor
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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