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03 | The Dev Environment

The repository is instrumented with dev container configuration that provides a consistent pre-built development environment deployed in a Docker container. Launch this in the cloud with GitHub Codespaces, or in your local device with Docker Desktop.

Dev Tools

In addition, we make use of these tools:

  • Visual Studio Code as the default editor | Works seamlessly with dev containers. Extensions streamline development with Azure and Prompt Flow.
  • Azure Portal for Azure subscription management | Single pane of glass view into all Azure resources, activities, billing and more.
  • Azure AI Studio (Preview) | Single pane of glass view into all resources and assets for your Azure AI projects. Currently in preview (expect it to evolve rapidly).
  • Azure ML Studio | Enterprise-grade AI service for managing end-to-end ML lifecycle for operationalizing AI models. Used for some configuration operations in our workshop (expect support to move to Azure AI Studio).
  • Prompt Flow | Open-source tooling for orchestrating end-to-end development workflow (design, implementation, execution, evaluation, deployment) for modern LLM applications.

Required Resources

We make use of the following resources in this lab:

Azure Samples Used | Give them a ⭐️ on GitHub

Azure Resources Used | Check out the Documentation

  • Azure AI Resource - Top-level Azure resource for AI Studio, establishes working environment.
  • Azure AI Project - saves state and organizes work for AI app development.
  • Azure AI Search - get secure information retrieval at scale over user-owned content
  • Azure Open AI - provides REST API access to OpenAI's powerful language models.
  • Azure Cosmos DB - Fully managed, distributed NoSQL & relational database for modern app development.
  • Deployment Models Deployment from model catalog by various criteria.