This comprehensive course equips you with skills to leverage Azure for building and deploying Large Language Model (LLM) applications. Learn to use Azure OpenAI Service for deploying LLMs, utilizing inference APIs, and integrating with Python. Explore architectural patterns like Retrieval-Augmented Generation (RAG) and Azure services like Azure Search for robust applications. Gain insights into streamlining deployments with GitHub Actions. Apply your knowledge by implementing RAG with Azure Search, creating GitHub Actions workflows, and deploying end-to-end LLM applications. Develop a deep understanding of Azure's ecosystem for LLM solutions, from model deployment to architectural patterns and deployment pipelines.
End to End LLMs with Azure
End to End LLMs with Azure
Taught in English
Intermediate level
Recommended experience
9 hours to complete
3 weeks at 3 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Create Large Language Model endpoints in Azure
Use GitHub Actions to deploy a containerized application for LLMs
Skills you'll gain
Details to know
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April 2024
Assessments
2 assignments
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There is 1 module in this course
This week, you will explore architectural patterns and deployment of large language model applications. By studying RAG, Azure services, and GitHub Actions, you will learn how to build robust applications. You will apply your learning by implementing RAG with Azure search, creating GitHub Actions workflows, and deploying an end-to-end application.
What's included
16 videos9 readings2 assignments1 ungraded lab
Offered by
Recommended if you're interested in Machine Learning
University of Colorado Boulder
Johns Hopkins University
Duke University
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