TL;DR

AI deployment requires more than pushing code. Plan for model validation, staged rollouts, monitoring setup, and rollback capability. Each stage has checkpoints that must pass before proceeding. Build deployment as a process, not an event.

Why it matters

AI systems fail in production in ways that are hard to predict from development environments. Careful deployment practices catch issues before they affect users at scale. The cost of fixing production issues is orders of magnitude higher than catching them during deployment.

Deployment lifecycle stages

Stage 1: Pre-deployment

Before any deployment begins:

Model validation:

  • Performance meets requirements
  • Bias testing completed
  • Safety testing passed
  • Edge cases evaluated

Documentation:

  • Model card complete
  • Deployment runbook ready
  • Monitoring plan defined
  • Rollback plan documented

Infrastructure:

  • Resources provisioned
  • Scaling configured
  • Monitoring instrumented
  • Logging enabled

Approvals:

  • Technical review complete
  • Ethics/bias review (if required)
  • Security review (if required)
  • Stakeholder sign-off

Stage 2: Staging environment

Deploy to staging first:

Environment requirements:

  • Production-like configuration
  • Representative data (sanitized)
  • Full monitoring stack
  • Realistic load patterns

Testing in staging:

  • Functional tests pass
  • Performance under load
  • Integration with dependencies
  • Error handling works
  • Monitoring captures issues

Exit criteria:

  • No blocking issues
  • Performance acceptable
  • All tests pass
  • Monitoring working

Stage 3: Shadow deployment

Run alongside production without serving users:

Shadow mode operation:

  • Receive real production traffic
  • Process requests normally
  • Compare outputs to current production
  • Don't serve responses to users

What to evaluate:

  • Output quality comparison
  • Performance comparison
  • Resource usage
  • Error patterns
  • Edge case handling

When to use shadow deployment:

  • Significant model changes
  • New architectures
  • Risk-sensitive applications
  • When you need production data validation

Stage 4: Canary deployment

Serve small percentage of real traffic:

Canary strategy:

Hour 0: 1% traffic
Hour 4: 5% traffic (if stable)
Hour 12: 25% traffic (if stable)
Hour 24: 50% traffic (if stable)
Hour 48: 100% traffic (if stable)

Monitoring during canary:

  • Compare error rates (canary vs. stable)
  • Compare latency distributions
  • Compare output quality metrics
  • Watch user feedback/complaints

Rollback triggers:

  • Error rate > 2x baseline
  • Latency > 1.5x baseline
  • Quality metrics degraded
  • User complaints spike

Stage 5: Full deployment

Complete the rollout:

Full deployment activities:

  • Gradually shift remaining traffic
  • Monitor continuously
  • Keep rollback ready
  • Communicate completion

Post-deployment:

  • Verify all metrics stable
  • Close deployment ticket
  • Update documentation
  • Archive artifacts

Deployment checklist

Pre-deployment checklist

  • Model validation complete and passing
  • Bias testing complete and acceptable
  • Safety testing complete and passing
  • Performance benchmarks met
  • Documentation complete
  • Rollback plan documented and tested
  • Monitoring dashboards ready
  • Alerting configured
  • Required approvals obtained

Deployment day checklist

  • Team available for deployment window
  • Communication channels open
  • Rollback procedure verified
  • Monitoring dashboards open
  • Previous deployment artifacts available
  • Stakeholders notified

Post-deployment checklist

  • All metrics within acceptable ranges
  • No elevated error rates
  • No user complaints
  • Monitoring working correctly
  • Documentation updated
  • Deployment retrospective scheduled

Rollback strategy

When to rollback

Automatic rollback triggers:

  • Error rate exceeds threshold
  • Latency exceeds threshold
  • Health checks fail
  • Resource exhaustion

Manual rollback triggers:

  • Quality degradation detected
  • User complaints
  • Harmful outputs discovered
  • Security concerns

Rollback execution

Quick rollback (minutes):

  • Traffic routing change
  • Keep new version available
  • Monitor old version stability

Full rollback (longer):

  • Redeploy previous version
  • Verify previous version stable
  • Investigate new version issues

Rollback testing

Test regularly:

  • Include rollback in deployment rehearsals
  • Verify rollback works in staging
  • Time your rollback procedure
  • Document any issues

Deployment patterns

Blue-green deployment

Two identical environments:

  • Blue: Current production
  • Green: New version

Switch traffic between them for instant cutover and rollback.

Best for: When you need instant rollback capability

Rolling deployment

Gradually replace instances:

  • Update instances one at a time
  • Monitor each update
  • Continue if stable

Best for: Large deployments where gradual transition is preferred

Feature flags

Control features independently of deployment:

  • Deploy code with feature disabled
  • Enable gradually via flag
  • Disable quickly if problems

Best for: Separating deployment from release

Common mistakes

Mistake Consequence Prevention
Skip staging Issues discovered in production Always use staging
Big bang deployment Hard to isolate problems Gradual rollout
No rollback plan Stuck with broken system Plan and test rollback
Insufficient monitoring Issues go undetected Comprehensive observability
Deploy on Friday Weekend incidents Deploy early in week

What's next

Build robust operations: