Deployment Best Practices

This guide provides best practices for deploying, managing, and optimizing your AI services on the Unicron platform.

Selecting the Right Deployment Type

• Serverless: Choose for variable workloads, cost efficiency, and minimal management overhead
• Dedicated: Choose for consistent workloads, specific hardware requirements, and predictable performance

Pre-Deployment Checklist

• Validate your service functionality locally before deployment
• Optimize your Docker image size to reduce startup times
• Ensure your service has proper error handling and logging
• Verify memory and CPU requirements through local testing
• Include health check endpoints in your service

Performance Optimization

Docker Image Optimization

• Use multi-stage builds to minimize image size
• Include only necessary dependencies
• Use appropriate base images (Alpine for smaller footprint)
• Pre-compile models and assets when possible

Model Optimization

• Quantize models where appropriate to reduce memory footprint
• Consider distilled or optimized model variants
• Use appropriate batch sizes for throughput vs. latency tradeoffs
• Implement caching for repetitive operations

Request Handling

• Implement proper timeouts for external dependencies
• Use connection pooling for database or API connections
• Implement backoff strategies for retries
• Consider batching requests for higher throughput

Monitoring and Alerting

• Set up alerts for abnormal error rates and latency spikes
• Monitor resource utilization to detect bottlenecks
• Track cost metrics to avoid unexpected charges
• Set up log-based alerts for critical application errors

Security Best Practices

• Implement proper authentication for your API endpoints
• Use environment variables for sensitive configuration
• Regularly update dependencies to address security vulnerabilities
• Implement proper input validation to prevent attacks
• Use least-privilege principles for service permissions

Scaling Strategy

• Set appropriate minimum instances for critical services
• Configure appropriate scaling thresholds based on service characteristics
• Consider scheduled scaling for predictable traffic patterns
• Test scaling behavior under load before production use

Deployment Strategies

• Implement blue-green or canary deployments for critical services
• Test new deployments in staging environments before production
• Consider using feature flags for gradual rollouts
• Maintain version control for deployment configurations

Cost Optimization

• Use serverless for variable or low-traffic workloads
• Implement scale-to-zero for non-critical services
• Monitor and set budget alerts to avoid unexpected costs
• Balance performance requirements with cost considerations
• Consider reserved instances for consistent, long-term workloads

Disaster Recovery

• Document your deployment configurations
• Set up scheduled backups for stateful services
• Test restoration procedures periodically
• Implement multi-region strategies for critical services