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Enterprise AI Development with GitHub Models and Azure

Enterprise AI Development with GitHub Models and Azure

2h 24mIntermediate2024-10-28

Authors

Rob Bos

Rob Bos

DevOps Professional, Consultant, Speaker, Microsoft MVP.

Course details

GitHub Models is a great place for developers to experiment with large language models (LLMs), especially if you’re an Azure developer who’s looking to test an idea or prototype an application. Integrating GitHub Models with Azure allows you to move logic back and forth as you work through each phase of a project. In this course, instructor Rob Bos shows you how to create prototypes and development paths in GitHub Models and then scale them for deployment in Azure. Along the way, gather insights to help you leverage the power of planning and practice, shifting your attention away from the more constrained options of GitHub Models to the more open and flexible features offered by Azure. This course is an ideal fit for Azure developers looking for a quick start guide to GitHub Models.

Learning objectives
Show Azure developers easy prototyping and development paths in GitHub Models.
Establish connections and workflows between GitHub Models and Azure.
Demonstrate a complete transition from GitHub Models experimentation to Azure deployment.

Skills covered

Version ControlGitHubNatural Language Processing (NLP)AzureSoftware Development ToolsArtificial Intelligence (AI)MicrosoftSoftware DevelopmentOne-Off

Concepts

0. Introduction

  • 01 - AI with GitHub Models

1. LLM Introduction

  • 02 - What is an LLM
  • 03 - Tokenization
  • 04 - Model sizes
  • 05 - Limits of GitHub Models
  • 06 - Content filters
  • 07 - Prompting an LLM
  • 08 - Different AI interaction modals

2. Exploring the GitHub Marketplace

  • 09 - Overview of the marketplace
  • 10 - Exploring models on the marketplace
  • 11 - Tags
  • 12 - Model details
  • 13 - Supported languages and SDK's

3. Using the Playground

  • 14 - Exploring the playground
  • 15 - Running the examples locally
  • 16 - What is a GitHub Codespace
  • 17 - Getting started inside a GitHub Codespace

4. Get Started with Implementation

  • 18 - Completions, streaming, and multi-turn
  • 19 - Embeddings and functions
  • 20 - Exploring the Jupyter Notebook examples
  • 21 - Retrieval-augmented generation
  • 22 - Data extraction transformation

5. Deploying to Azure

  • 23 - Architectural overview
  • 24 - Rolling out Azure resources
  • 25 - Validating the solution with the Azure resources

6. Production

  • 26 - Monitoring your solution in production
  • 27 - Setting up rate limits

Conclusion

  • 28 - Responsible AI

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