Module 925 minutes
Deployment and Scaling
Deploy AI products to production and scale reliably. Handle traffic spikes and ensure uptime.
deploymentscalingdevopsproduction
Learning Objectives
- ✓Deploy AI applications
- ✓Handle traffic scaling
- ✓Implement monitoring
- ✓Ensure reliability
Ship It and Scale It
Deploy confidently and handle growth.
Deployment Checklist
- API keys in environment variables
- Error handling implemented
- Rate limiting configured
- Monitoring in place
- Backup plan ready
- Cost alerts set
Scaling Strategies
Queue-based processing:
- Async for non-real-time
- Handle spikes gracefully
- Batch when possible
Load balancing:
- Distribute requests
- Multiple API keys
- Failover providers
Caching:
- Redis for results
- CDN for static content
- Database query optimization
Monitoring
- API response times
- Error rates
- Token usage
- User satisfaction
- Cost per user
Key Takeaways
- →Use environment variables for all secrets
- →Implement queuing for scalability
- →Monitor everything: errors, latency, costs
- →Have fallback providers ready
- →Test under load before launch
Practice Exercises
Apply what you've learned with these practical exercises:
- 1.Set up production deployment
- 2.Implement queue system
- 3.Configure monitoring
- 4.Load test your API