Learn to build and deploy Large Language Model applications using Azure OpenAI Service, implement RAG patterns, and automate deployments with GitHub Actions.
Learn to build and deploy Large Language Model applications using Azure OpenAI Service, implement RAG patterns, and automate deployments with GitHub Actions.
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:
English
English
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
Instructor

20 Courses
A Technology Educator and Former Olympic Athlete Pioneering AI Innovation
Alfredo Deza embodies a unique combination of athletic excellence and technological expertise, transitioning from a distinguished career as Peru's first World Junior Champion in high jump and 2004 Olympian to becoming a leading voice in technology education and development. Currently serving as a Principal Cloud Advocate at Microsoft and Adjunct Assistant Professor at Duke University's Pratt School of Engineering, Deza has built an impressive career spanning nearly two decades in software engineering and education. His academic contributions extend through guest lectures at prestigious institutions including Oxford University, Georgia Tech, and Carnegie Mellon University, where he shares expertise in machine learning, cloud computing, and programming languages. As an accomplished author, he has co-authored several influential books with O'Reilly Media, including "Practical MLOps" and "Python for DevOps," while developing comprehensive courses on Coursera covering topics from large language models to Rust programming. His teaching portfolio at Duke includes graduate-level courses in machine learning operations and Python programming, reflecting his commitment to making complex technical concepts accessible. Deza's expertise encompasses a broad spectrum of technologies, including Azure, MLOps, DevOps, Python, Rust, and Databricks, which he leverages to bridge the gap between academic theory and industry practice. His unique perspective, shaped by his background as an Olympic athlete, influences his approach to teaching and technology, emphasizing the importance of continuous learning and knowledge sharing in the rapidly evolving field of artificial intelligence and cloud computing.
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