In this quest, you’ll take your locally running Gen AI app and deploy it to the cloud using Azure. You’ll use the move from GitHub Models to Azure AI Foundry — all with just one click of a button. This is where your prototype becomes real.
Whether you’re prepping for a demo, testing in production-like conditions, or just want to share your app with others, this quest is your launchpad.
👉 Want to catch up on the full program or grab more quests? https://aka.ms/JSAIBuildathon
💬 Got questions or want to hang with other builders? Join us on Discord — head to the #js-ai-build-a-thon channel.
Why this quest matters?
- You’ll learn how to go from local to cloud in minutes
- You’ll get hands-on with Azure AI Foundry (Directly from VS Code)
- You’ll build confidence deploying real AI apps on enterprise ready infrastructure
What You’ll Need to Complete the Quest
✅ A GitHub account
✅ An Azure subscription (https://azure.microsoft.com/free/) or (https://azure.microsoft.com/en-us/free/students) for students
✅ Visual Studio Code installed
That’s it. You’ll be live before your coffee gets cold.
Concepts You’ll Explore (And Why They Matter)
🛠 GitHub Models to Azure AI Foundry
GitHub Models is a developer-friendly playground to explore and test AI models.
Azure AI Foundry is your enterprise-grade platform for building, evaluating, and deploying AI models at scale. Think of it as your mission control for GenAI, where experimentation meets production.
To migrate from GitHub Models to Azure AI Foundry, is just a click of a button.
Welcome to Azure AI Foundry
🕸️ AI Foundry VS Code Extension
The Azure AI Foundry VS Code extension is a powerful new tool designed to streamline the development of intelligent applications directly within your coding environment. This extension allows developers to build, test, and deploy AI agents and applications using the same modular, production-grade components found in the Azure AI Foundry portal.
Whether you're customizing foundation models or managing agentic behavior, the extension brings the full power of Azure AI Foundry into your local development loop.
🔁 Best Security Practices
When using Azure AI Foundry, securing your keys, endpoints, and data flows is essential to building trustworthy and compliant AI applications. First, always store API keys and connection strings in Azure Key Vault, and if using a local .env file, ensure you add it to your .gitignore file.
Bonus resources to keep going
Here’s a curated list of follow-up reads and lessons to deepen your knowledge after this quest. Each one is designed to build your skills and spark new ideas:
- About GitHub Models - Get the lowdown on what GitHub Models are, why they matter, and how they fit into your workflow.
- Install the Microsoft AI Tools Extension Pack: A set of must-have VS Code extensions for Gen AI workflows.
- Azure AI Foundry Extension for Visual Studio Code is Available in Preview. AI Foundry extension preview announcement
- Work with the Azure AI Foundry for Visual Studio Code extension docs. Learn about additional features of the Azure AI Foundry for Visual Studio Code extension such as code templates, playgrounds, and integration with other VS Code extensions.
- 📹 BRK107: Develop, Build and Deploy LLM Apps using GitHub Models and Azure AI Foundry. Watch this demo-packed session to learn how to quickly experiment and compare LLMs using GitHub Models, build functionality into your application, and scale up to production deployment with Azure AI Foundry
This quest is where your AI development in the cloud journey truly begins. Just you, your code editor, and a model ready to turn your ideas into something real. So, jump in, build securely in the cloud, and discover just how far JavaScript and AI can take you.
💬 Connect with the community on Discord
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