Deploy, Monitor & Scale
Ship voice AI to production
Master end-to-end production deployment for voice AI with Docker, LiveKit Cloud, monitoring, auto-scaling, alerting, cost optimization, and blue-green deployments.
What You Build
Production deployment pipeline with monitoring dashboard, auto-scaling, and alerting.
Prerequisites
- →Course 1.1
Deployment architecture
15mUnderstand deployment architecture options including cloud, self-hosted, and hybrid approaches.
Docker configuration
20mConfigure Docker with multi-stage builds and optimization for voice agent containers.
LiveKit Cloud deployment
20mDeploy to LiveKit Cloud with the CLI, manage secrets, and handle rollbacks.
Monitoring setup
25mSet up monitoring with Cloud Insights, transcript analysis, and distributed traces.
Custom metrics & data hooks
20mImplement custom metrics with data hooks and Prometheus for detailed observability.
Alerting & incident response
20mConfigure alerting with PagerDuty integration and incident response runbooks.
Multi-tenant architecture
25mBuild a multi-tenant platform on LiveKit: room namespacing for isolation, JWT metadata for per-tenant agent config, dynamic tool registration, usage tracking for billing, and scaling shared worker pools.
Auto-scaling
20mSet up auto-scaling with policies, load balancing, and capacity planning.
Cost optimization
15mOptimize costs through token usage tracking, model selection, and intelligent caching.
Blue-green deployments
20mImplement blue-green deployments with zero-downtime releases, canary testing, and feature flags.
Operational runbook
15mCreate operational runbooks with deployment checklists and post-mortem templates.
What You Walk Away With
End-to-end production deployment with monitoring, scaling, alerting, and operational best practices.