Model Deployment
Deployment Strategies
Maintain two identical production environments (blue and green), switching traffic instantly between them for zero-downtime deployments.
- Two identical production environments
- Instant traffic switching
- Complete environment isolation
- Fast rollback capability
- Zero-downtime deployments
- Critical production updates
- Major model version changes
- Infrastructure migrations
- Scenarios requiring instant rollback
Gradually roll out new model to increasing percentage of traffic, monitoring metrics before proceeding to full deployment.
- Gradual traffic increase (5% → 25% → 50% → 100%)
- Automated rollback on metric degradation
- Risk mitigation through phased rollout
- Real user feedback at each stage
- Configurable promotion criteria
- Production ML model updates
- New feature releases
- Performance-sensitive changes
- Risk-averse deployments
Deploy new model alongside production, sending duplicated traffic to both without affecting users, for safe testing.
- Zero user impact
- Real production traffic patterns
- Side-by-side performance comparison
- Production environment validation
- No rollback needed (not serving users)
- New model validation
- Performance benchmarking
- Regression testing in prod
- Pre-launch confidence building
Split traffic between two model versions to compare performance, business metrics, and user experience statistically.
- Statistical significance testing
- Configurable traffic splits
- Business metric tracking
- User segmentation support
- Multi-variant support (A/B/C)
- Model performance comparison
- Algorithm experimentation
- User experience optimization
- ROI-focused deployments
Continuously compare new model (challenger) against current best (champion), automatically promoting better performers.
- Ongoing performance comparison
- Automatic promotion on success
- Multiple challenger support
- Metric-based decision making
- Built-in experimentation
- Continuous model improvement
- AutoML model selection
- Algorithm optimization
- Adaptive systems
Dynamically allocate traffic based on real-time performance, automatically optimizing for best-performing model variant.
- Dynamic traffic allocation
- Exploration vs exploitation balance
- Real-time optimization
- Adaptive to performance changes
- Minimizes opportunity cost
- Recommendation optimization
- Content ranking
- Ad serving optimization
- Dynamic model selection
Model Testing Strategies
| Strategy | Traffic Split | Risk Level | Rollback | Use Case |
|---|---|---|---|---|
| Shadow Deployment | 0% to new model (observational) | Very Low | N/A (no prod traffic) | Test new model without risk |
| A/B Testing | 50/50 or custom split | Medium | Route traffic to old model | Compare model performance |
| Canary | 5-20% to new model gradually | Low-Medium | Fast - reduce traffic | Gradual rollout with safety |
| Blue-Green | 0% or 100% | High | Fast - switch back | Quick cutover with instant rollback |
| Multi-Armed Bandit | Dynamic based on performance | Medium | Automatic reduction | Optimize performance automatically |
Progressive Rollout Phases
Phase 1: Internal
Audience: Dev/QA team
Traffic: 0%
Duration: Days
Test basic functionality
Phase 2: Alpha
Audience: Power users
Traffic: 1-5%
Duration: 1-2 weeks
Real-world validation
Phase 3: Beta
Audience: Select regions
Traffic: 10-25%
Duration: 1-2 weeks
Performance at scale
Phase 4: GA
Audience: All users
Traffic: 100%
Duration: Ongoing
Full production rollout
Pre-Deployment Checklist
Model Validation
- ✓ Offline metrics meet thresholds
- ✓ Model size appropriate for serving
- ✓ Latency requirements validated
- ✓ Bias and fairness checks passed
- ✓ Security scanning completed
- ✓ Input validation tested
Infrastructure
- ✓ Resource limits configured
- ✓ Auto-scaling policies set
- ✓ Health checks implemented
- ✓ Monitoring dashboards ready
- ✓ Alerting rules configured
- ✓ Rollback plan documented
Observability
- ✓ Metrics collection enabled
- ✓ Logging properly configured
- ✓ Distributed tracing setup
- ✓ Error tracking integrated
- ✓ Performance profiling ready
- ✓ Cost tracking enabled
Process
- ✓ Change request approved
- ✓ Stakeholders notified
- ✓ Deployment window scheduled
- ✓ Runbook updated
- ✓ On-call rotation set
- ✓ Communication plan ready
Rollback Strategies
Traffic Shifting
Speed: Immediate
Method: Adjust load balancer weights to route traffic back to stable model version.
Pros: Instant, no downtime
Cons: Requires both versions running
Model Registry Pointer
Speed: Fast (seconds)
Method: Update model registry to point to previous version, reload endpoints.
Pros: Simple, version controlled
Cons: Brief service interruption
Infrastructure Rollback
Speed: Slower (minutes)
Method: Redeploy previous container/pod configuration via IaC or K8s.
Pros: Full infrastructure revert
Cons: Takes longer, more complex