Coding claude-3-haiku

ML Pipeline: FastAPI, Celery, RabbitMQ

Design a scalable ML pipeline with Python, FastAPI, Celery, and RabbitMQ. Get code, docs, and architecture for production. Start building now!

8.9

Performance Score

2,390ms response time

60 views

0 copies

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: 10M messages/day

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.

SCALE: Design for handling millions of users/transactions. [Ref: e38095ef]