🚀 Model Deployment Guide
Welcome to the Model Deployment section of AI Engineering Academy! This module will guide you through the practical aspects of deploying AI models in production environments.
📚 Current Content
Quantization Techniques
| Notebook | Description |
|---|---|
| AWQ Quantization | Activation-aware Weight Quantization implementation |
| GGUF Quantization | GGUF format quantization guide |
🔜 Coming Soon!
We're actively working on comprehensive deployment guides covering:
1. 🌐 Cloud Deployment
- AWS SageMaker integration
- Azure ML deployment
- Google Cloud AI Platform
- Custom cloud solutions
2. 🛠️ Optimization Techniques
- Model pruning
- Knowledge distillation
- Additional quantization methods
- Inference optimization
3. 📦 Containerization
- Docker implementation
- Kubernetes orchestration
- Container optimization
- Scaling strategies
4. 🔄 CI/CD Pipelines
- Automated testing
- Deployment automation
- Model versioning
- Monitoring setup
5. 🎯 Edge Deployment
- Mobile deployment
- Edge device optimization
- Embedded systems
- IoT integration
6. ⚡ Performance Optimization
- Latency reduction
- Throughput optimization
- Resource management
- Cost optimization
Stay tuned for regular updates as we add more content and practical examples!
🤝 Contributing
Interested in contributing to this section? We welcome:
- Additional deployment strategies
- Case studies
- Performance optimization techniques
- Best practices documentation
See our contributing guidelines for more information.
📝 License
This project is licensed under the MIT License - see the LICENSE file for details.
Coming Soon: Complete deployment guides for production AI systems!
Made with ❤️ by the AI Engineering Academy Team
Made with ❤️ by the AI Engineering Academy Team