Azure AI Engineering Hub: The Ultimate Unified Catalog

Welcome to the Azure AI Engineering Hub, a highly structured, comprehensive, all-in-one repository designed for modern Cloud AI Engineers. This hub centralizes official labs, production-grade samples, and advanced orchestration patterns into a definitive service-based hierarchy.

🙏 Credits, Licenses, & Attribution

This repository serves as a centralized gateway to the Microsoft AI ecosystem. We give full credit and special thanks to the Microsoft Learning (MSLearn), Azure Samples, teams.

For detailed licensing information (MIT, CC0 1.0) and full community attributions, please refer to: ➡️ ACKNOWLEDGEMENTS.md

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🏗️ The 13-Pillar Service Hierarchy

To ensure a smooth development experience, every Azure AI service is separated into its own definitive pillar:

Core AI Services

1. 01-Azure-OpenAI: The heart of Generative AI. Chat completions, DALL-E, embeddings, and enterprise-grade proxies.
2. 02-Azure-AI-Search: Advanced Vector Search, Hybrid Search, and RAG patterns with LangChain.
3. 03-Azure-AI-Language: NLP, Conversational Language Understanding (CLU), and Text Analytics.
4. 04-Azure-AI-Speech: Speech-to-Text (STT), Text-to-Speech (TTS), and real-time Speech Translation.
5. 05-Azure-AI-Vision: Image classification, Object detection, and OCR.
6. 06-Azure-AI-Video: Specialized folder for Azure AI Video Indexer and video analysis.
7. 07-Azure-Document-Intelligence: Automated form extraction, PDF layout analysis, and document AI.

Conversational & Agentic AI

8. 08-Azure-AI-Bots: Bot Framework, Bot Composer, and conversational design patterns.
9. 09-Azure-AI-Foundry: The new Azure AI Foundry (formerly Studio) SDKs and Model Catalog integration.
10. 10-Azure-AI-Agents: Advanced orchestration with Semantic Kernel, RAG-Agents-Accelerator, and multi-agent systems.
11. 11-Azure-AI-Assistants: Task-specific AI assistant implementations.

Management & Deployment

12. 12-MLOps-Governance-Eval: Production monitoring, Fine-tuning, Prompt Flow, and AI model evaluation.
13. 13-Basic-AI-Foundations: Core concepts, AI-900 (Fundamentals), and the AI-102 (Engineer) certification base.

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🛠️ Getting Started for Engineers

1. Refresh the Catalog

If you need to re-sync or clone missing repositories, run the master setup script:

./setup_azure_ai_repos.sh

2. Configure Python Virtual Environment

We highly recommend using a virtual environment (venv) to isolate dependencies for this project.

Create the virtual environment:

python -m venv venv

Activate the virtual environment:

• On macOS / Linux:

  source venv/bin/activate

• On Windows (Command Prompt):

  venv\Scripts\activate.bat

• On Windows (PowerShell):

  .\venv\Scripts\Activate.ps1

(To exit the virtual environment later, simply run the command deactivate)

3. Install Dependencies

With your virtual environment activated, this project provides a recursive installer that discovers and installs all requirements.txt files across all pillars:

python3 install_all_requirements.py --install

4. Configure Environments

Use the provided .env.template (or .env.example files localized within individual lab folders) as a baseline.

1. Copy the .env.example to .env.

   cp .env.example .env

2. Open the new .env file and set your global Azure credentials and endpoint values. (Note: .env files are ignored by git to protect your secrets).

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📦 Unified Resources

This hub is the only repo you need to master Azure AI. It contains:

• All Docs: Service-specific documentation links and READMEs.
• All Labs: 24+ official Microsoft certification labs.
• All Code: 27+ advanced engineering repositories from the Azure ecosystem.

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Created for Azure AI Engineers & Cloud Developers.