Enterprise AI Orchestration Platform (EAOP)

A sophisticated AI platform that orchestrates multiple AI models and services, designed for enterprise-scale applications. This platform demonstrates advanced AI architecture patterns and best practices for production deployments.

Created by: Kakachia777

Features

• Multiple AI Model Support

  • GPT-4o Integration for advanced text processing
  • Computer Vision capabilities using YOLOv8
  • Sentence Embeddings for semantic search and similarity

• Enterprise-Grade Architecture

  • Scalable FastAPI backend
  • Asynchronous processing
  • Comprehensive logging and monitoring
  • MLOps integration with MLflow

• Security

  • OAuth2 authentication
  • JWT token-based authorization
  • CORS support
  • SSL/TLS encryption

• Monitoring & Observability

  • Prometheus metrics
  • MLflow experiment tracking
  • Comprehensive logging

Technical Stack

• Core AI/ML

  • OpenAI GPT-4o
  • PyTorch
  • Transformers
  • Sentence-Transformers

• Backend

  • FastAPI
  • Uvicorn
  • Python 3.9+

• Monitoring

  • MLflow
  • Prometheus
  • Grafana

• Data Storage

  • MongoDB
  • Redis

Getting Started

1. Clone the repository:

git clone https://github.com/Kakachia777/enterprise-ai-platform.git
cd enterprise-ai-platform

2. Create a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

4. Set up environment variables:

cp .env.example .env
# Edit .env with your configuration

5. Start the application:

python src/api/main.py

API Documentation

Once the application is running, visit:

• Swagger UI: http://localhost:8000/docs
• ReDoc: http://localhost:8000/redoc

Architecture Overview

The platform follows a modular architecture:

• src/core/: Core AI orchestration and configuration
• src/api/: FastAPI application and endpoints
• config/: Configuration files
• models/: Model artifacts and caches
• data/: Data storage directories
• tests/: Test suites

MLOps Integration

The platform includes comprehensive MLOps capabilities:

1. Experiment Tracking

   • All model runs are tracked in MLflow
   • Metrics, parameters, and artifacts are logged automatically

2. Model Monitoring

   • Real-time performance metrics
   • Model drift detection
   • Resource utilization tracking

3. Deployment Pipeline

   • Containerized deployment support
   • Model versioning
   • A/B testing capabilities

Security Considerations

1. Authentication & Authorization

   • OAuth2 with JWT tokens
   • Role-based access control
   • Token refresh mechanism

2. Data Security

   • Encrypted data storage
   • Secure API endpoints
   • Input validation and sanitization

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

License

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

Contact

For any queries or support, please contact:

• Owner: Kakachia777
• Issue Tracker: GitHub Issues