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ML Pipeline: FastAPI, Celery, RabbitMQ v4

Generate a scalable machine learning pipeline with Python, FastAPI, Celery, and RabbitMQ. Get code, diagrams, and deployment strategies now!

9.9

Performance Score

3,274ms response time

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Last tested: 5 months ago

The Prompt

You are a senior backend engineer. Design and implement a complete, scalable machine learning pipeline using Python + FastAPI + Celery + RabbitMQ.

ARCHITECTURE REQUIREMENTS:
- Technology: Python + FastAPI + Celery + RabbitMQ
- Features: load balancing
- Scale: 1M concurrent users

IMPLEMENTATION REQUIREMENTS:
1. Complete system architecture with diagrams
2. API design (REST/GraphQL/gRPC)
3. Real-time communication setup
4. Database design (SQL/NoSQL/hybrid)
5. Caching strategy (Redis/Memcached)
6. Message queue implementation
7. Authentication and authorization
8. Rate limiting and DDoS protection
9. Monitoring and alerting
10. Load testing and optimization
11. Deployment strategy (Docker, Kubernetes)
12. Disaster recovery plan

DELIVERABLES:
- Complete backend codebase
- API documentation
- Database schemas
- Infrastructure as Code (Terraform/CloudFormation)
- Docker/Kubernetes configs
- Monitoring dashboards
- Load testing scripts
- Architecture documentation

Generate a production-ready, scalable system with all components, documentation, and best practices.

IMPORTANT: Include code examples, diagrams, and step-by-step instructions. [Ref: c0d0ee60]