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Alfredo Deza

In this comprehensive course, you will:

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In this comprehensive course, you will:

  • Learn to use Azure OpenAI Service for deploying LLMs and integrating with Python
  • Explore architectural patterns like Retrieval-Augmented Generation (RAG) for robust applications
  • Utilize Azure services like Azure Search to enhance LLM capabilities
  • Gain insights into streamlining deployments with GitHub Actions automation
  • Apply your knowledge by implementing end-to-end LLM solutions

Develop a deep understanding of Azure's ecosystem for LLM solutions, from model deployment to architectural patterns and deployment pipelines. Gain the skills to build and deploy powerful LLM applications in real-world scenarios.

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What's inside

Learning objectives

  • Deploying llms with azure openai service
  • Integrating azure openai apis with python
  • Implementing retrieval-augmented generation (rag) with azure search
  • Automating testing and deployment using github actions
  • Building end-to-end llm applications on azure

Syllabus

\\- Module 1 (8 hours to complete)
\\- Video: Meet your Course Instructor: Alfredo Deza (1 minute, preview module)
\\- Video: Introduction (0 minutes)
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\\- Video: Getting access to Azure OpenAI Service (3 minutes)
\\- Video: Creating an Azure OpenAI Service Resource (5 minutes)
\\- Video: Deploy an OpenAI Model (4 minutes)
\\- Video: Using keys and endpoints (5 minutes)
\\- Video: Creating a simple Python example (5 minutes)
\\- Video: Reviewing usage and quotas (2 minutes)
\\- Video: Architectural overview (3 minutes)
\\- Video: Overview of Azure AI Search (3 minutes)
\\- Video: Automation and deployment with GitHub (4 minutes)
\\- Video: Setting up Azure components (3 minutes)
\\- Video: Using RAG with Azure OpenAI (4 minutes)
\\- Video: Application overview (6 minutes)
\\- Video: Deployment and automation overview (4 minutes)
\\- Video: Using GitHub Actions with Azure (8 minutes)
\\- Reading: About this course (10 minutes)
\\- Reading: Connect with your instructor (10 minutes)
\\- Reading: Key Terms (10 minutes)
\\- Reading: External lab: Use the Azure OpenAI APIs (10 minutes)
\\- Reading: External lab: Create an Azure AI Search resource (10 minutes)
\\- Reading: External Lab: Deploy an end-to-end application (10 minutes)
\\- Assignment: Knowledge check (180 minutes)
\\- Assignment: Graded Quiz (180 minutes)

Good to know

Know what's good
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Teaches the use of the Azure OpenAI Service to develop AI applications
Provides hands-on experience in using GitHub Actions for deployment automation
Covers architectural patterns like Retrieval-Augmented Generation (RAG) for building robust applications
Shows how to leverage Azure services like Azure Search to enhance LLM capabilities
Taught by industry experts Alfredo Deza
Requires basic knowledge of Python and Azure services

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in End to End LLM with Azure with these activities:
Practice using Python for data manipulation and analysis
Enhance your programming skills by practicing data manipulation and analysis techniques in Python, which will be essential for working with LLM outputs.
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  • Solve coding challenges or practice problems.
  • Create small scripts to manipulate and analyze data.
  • Review online tutorials or documentation.
Review key concepts in natural language processing
Strengthen your foundation by reviewing essential concepts in natural language processing, enhancing your understanding of LLM capabilities and applications.
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  • Revisit textbooks or online resources on NLP.
  • Focus on topics such as tokenization, stemming, and part-of-speech tagging.
  • Complete practice exercises to reinforce your understanding.
  • Attend a workshop or seminar on NLP.
Collaborate with peers on LLM project ideas
Enhance your LLM project skills by brainstorming and sharing ideas with peers, providing valuable feedback and expanding your perspective.
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  • Connect with classmates or fellow LLM enthusiasts.
  • Share project ideas and discuss potential applications.
  • Collaborate on developing project plans and implementation strategies.
  • Provide constructive feedback and exchange knowledge.
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Explore advanced LLM capabilities using Microsoft's Learn tutorials
Reinforce your understanding of Azure OpenAI Service and enhance your LLM expertise by exploring Microsoft's comprehensive tutorials.
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  • Visit the Microsoft Learn portal and browse the Azure AI tutorials.
  • Identify tutorials focused on Azure OpenAI Service and LLM applications.
  • Follow the step-by-step instructions to build hands-on projects.
  • Experiment with different LLM capabilities and explore advanced usage patterns.
Create a blog post or article sharing your insights on LLM applications
Demonstrate your knowledge of LLMs and share your unique perspective by creating written content that will educate and inspire others.
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  • Research LLM applications and identify a specific topic to focus on.
  • Develop an outline and gather supporting evidence.
  • Write a compelling and well-structured article or blog post.
  • Proofread and edit your content to ensure clarity and accuracy.
  • Publish your article on a relevant platform and share it with your network.
Develop a step-by-step guide to using Azure OpenAI Service
Deepen your understanding of Azure OpenAI Service by creating a comprehensive guide that will benefit others and reinforce your own knowledge.
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  • Gather information and resources on Azure OpenAI Service.
  • Organize the content in a logical and user-friendly manner.
  • Write clear and concise instructions, providing code examples and visuals as needed.
  • Review and edit the guide to ensure accuracy and clarity.
  • Share the guide with peers or the broader developer community.
Build a full-stack LLM application from scratch
Challenge yourself and apply your LLM knowledge by creating a fully-functional application that leverages Azure OpenAI Service.
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  • Define the application's purpose and functionality.
  • Design the application architecture and select appropriate technologies.
  • Implement the application's core features using Azure OpenAI Service.
  • Develop the application's front-end and back-end components.
  • Test and deploy the application to a cloud platform.
Participate in an LLM hackathon or competition
Test your skills and gain valuable experience by participating in an LLM-focused hackathon or competition, pushing your limits and collaborating with others.
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  • Identify relevant LLM hackathons or competitions.
  • Form a team or work individually.
  • Develop an innovative LLM application or solution.
  • Submit your project and present it to judges.
  • Receive feedback and learn from other participants.

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