Industry Boilerplate Code & Use Cases

This document provides industry-standard boilerplate code and real-world use cases for each topic.

Quick Reference

• Topic 1: Basic Serving → Customer support, content generation
• Topic 2: Docker → Multi-cloud, CI/CD, edge deployment
• Topic 3: Kubernetes → Large-scale platforms, enterprise ML
• Topic 4: Load Testing → Performance validation, capacity planning
• Topic 5: vLLM → High-throughput production serving
• Topic 6: Autoscaling → Cost optimization, traffic handling
• Topic 7: Canary → Safe deployments, A/B testing
• Topic 8: Monitoring → Production observability, alerting
• Topic 9: Canary (same as 7)
• Topic 10: Versioning → Model management, rollback
• Topic 11: Drift Detection → Model health, quality assurance
• Topic 12: Triton → Multi-model serving, model pipelines

Common Industry Patterns

Pattern 1: API Gateway → Serving Layer

[API Gateway] → [Load Balancer] → [K8s Service] → [LLM Pods]

Used by: OpenAI, Anthropic, HuggingFace

Pattern 2: Model Registry → Serving

[Model Registry] → [CI/CD] → [K8s Deployment] → [Serving Pods]

Used by: MLflow, Weights & Biases, custom platforms

Pattern 3: Monitoring → Alerting → Auto-remediation

[Prometheus] → [Grafana] → [Alertmanager] → [PagerDuty/Slack]

Used by: All production ML systems

See individual topic READMEs for detailed boilerplate code and use cases.