Model Deployment with FastAPI

Deploy my trained ML model as a production API.

Model details:
- Model type: [YOUR MODEL]
- Framework: [PyTorch/TensorFlow/Sklearn]
- Input format: [DESCRIBE INPUT]
- Expected load: [REQUESTS PER SECOND]

API requirements:
1. FastAPI application:
   - Async request handling
   - Input validation with Pydantic
   - Error handling
2. Model serving:
   - Model loading and caching
   - Batch inference support
   - GPU utilization
3. Performance:
   - Request queuing
   - Connection pooling
   - Response caching
4. Monitoring:
   - Health checks
   - Prometheus metrics
   - Request logging
5. Security:
   - Authentication
   - Rate limiting
   - Input sanitization
6. Documentation:
   - OpenAPI/Swagger
   - Example requests
7. Containerization:
   - Dockerfile
   - Docker Compose
   - Kubernetes manifests

Include load testing and scaling strategies.