Azure AutoML API

A REST API built with FastAPI for interacting with Azure Machine Learning's AutoML capabilities. This service provides unified HTTP endpoints to manage datasets, run experiments, and deploy models.

Features

• Dataset Management: Upload and manage ML datasets
• AutoML Experiments: Launch and monitor automated ML experiments
• Model Management: Browse registered models and deployment endpoints
• Azure AD Integration: Production-ready authentication with token exchange
• MCP Server: Expose API as Model Context Protocol server for AI agents
• Background Tasks: Async processing for long-running operations

Quick Start

1. Install dependencies:

   uv sync

2. Set up environment (create .env file):

   JWT_SECRET=your-secret-key
   AZURE_TENANT_ID=your-tenant-id
   AZURE_CLIENT_ID=your-client-id
   AZURE_CLIENT_SECRET=your-client-secret
   AZURE_SUBSCRIPTION_ID=your-subscription-id
   AZURE_ML_WORKSPACE=your-workspace-name
   AZURE_ML_RESOURCE_GROUP=your-resource-group

3. Initialize database:

   uv run python scripts/create_tables.py

4. Start the server:

   uv run python -m automlapi.runserver

   Server runs at http://localhost:8005

Quick Testing

Generate a test token and try the API:

# Generate test token
uv run python scripts/create_token.py testuser dev-tenant 24

# Test endpoints
TOKEN="your-generated-token"
curl -H "Authorization: Bearer $TOKEN" http://localhost:8005/auth/me
curl -H "Authorization: Bearer $TOKEN" http://localhost:8005/datasets

API Endpoints

Core Endpoints

• POST /auth/exchange – Exchange Azure AD token for API token
• GET /auth/me – Get current user info
• GET|POST /datasets – Manage datasets
• GET|POST /experiments – Manage AutoML experiments
• GET /runs – List experiment runs
• GET /models – List registered models
• GET|POST /endpoints – Manage deployment endpoints

Documentation

• Swagger UI: http://localhost:8005/docs
• ReDoc: http://localhost:8005/redoc
• OpenAPI JSON: http://localhost:8005/openapi.json

Documentation

For detailed setup and usage instructions, see the documentation in the /docs folder:

• Installation Guide - Dependencies, prerequisites, and setup
• Authentication Guide - Azure AD integration and security
• Database Setup - Azure SQL Database configuration
• API Reference - Complete endpoint documentation
• Testing Guide - Testing approaches and examples
• Deployment Guide - Production deployment and MCP server
• RBAC Guide - Role-based access control system

Development vs Production

The API supports two modes:

• Local Development: Uses SQLite (ENVIRONMENT=local)
• Production: Uses Azure SQL Database (ENVIRONMENT=production)

Testing

Run the test suite:

uv run pytest

For comprehensive testing approaches including custom tokens, Postman, and automated testing, see the Testing Guide.

Architecture

Frontend (React/Vue/etc)
    ↓ (Azure AD authentication)
Azure AutoML API (FastAPI)
    ↓ (Service Principal auth)
Azure ML Workspace
    ↓ (Metadata storage)
Azure SQL Database

The API acts as a secure middleware layer between your frontend applications and Azure ML services, handling authentication, data management, and experiment orchestration.

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request

License

This project is licensed under the MIT License - see the LICENSE file for details.