Production Deployment Playbook
Technical checklist for deploying ML models to production safely and reliably. Infrastructure, monitoring, scaling, and disaster recovery.
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−Infrastructure & Architecture
Build scalable, resilient deployment infrastructure
Production ML systems require robust infrastructure that can scale with demand, maintain high availability, and support rapid iteration.
Container & Orchestration
- Docker containerization for reproducible deployments
- Kubernetes (EKS/GKE/AKS) for production orchestration
- Multi-region deployment for disaster recovery
- Auto-scaling policies based on CPU/memory/custom metrics
- Resource quotas and network policies for isolation
Model Serving Architecture
- Separate inference servers from training infrastructure
- API Gateway (Kong, AWS API Gateway) for rate limiting & auth
- Load balancing across multiple model replicas
- Caching layer (Redis) for frequently requested predictions
- Message queues (RabbitMQ, Kafka) for async workloads
- Model registry (MLflow, Seldon) for version management
Database & Storage
- Primary database (PostgreSQL) for operational data
- Data warehouse (Snowflake, BigQuery) for analytics
- Object storage (S3) for model artifacts and datasets
- Time-series database (InfluxDB, Prometheus) for metrics
- Real-time cache (Redis) for session/prediction caching
- Database replication for high availability
Networking & Security
- Private subnets for databases and internal services
- VPN/bastion hosts for admin access
- SSL/TLS for all data in transit
- WAF (Web Application Firewall) for API protection
- DDoS protection at network level
- Regular security audits and penetration testing
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+Deployment Strategy
Minimize risk and enable rapid iterations
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+Monitoring & Alerting
Real-time visibility into system health and performance
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+Scaling & Performance
Handle growth without degradation
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+Disaster Recovery & Reliability
Prepare for failures and minimize downtime
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+Operations & Maintenance
Keep systems healthy and up-to-date
Deployment Timeline
Pre-Deployment (1 day)
- Complete all testing & code reviews
- Run load tests
- Prepare rollback procedures
- Get stakeholder approval
Canary Phase (6-12 hours)
- Deploy to 5% traffic
- Monitor metrics closely
- Gradually increase to 100%
- Watch for issues
Post-Deployment (24 hours)
- Keep enhanced monitoring active
- Monitor for delayed issues
- Gather feedback from users
- Document lessons learned
Stabilization (1 week)
- Normalize monitoring
- Update documentation
- Train team on new version
- Plan next improvements
Critical Metrics to Monitor
System Health
- ✓Error rate < 1%
- ✓Latency p95 < 500ms
- ✓CPU utilization < 70%
- ✓Memory utilization < 80%
Model Performance
- ✓Accuracy > 90% baseline
- ✓Prediction latency < 100ms
- ✓Confidence avg > 75%
- ✓Data quality score > 95%
Reliability
- ✓Uptime > 99.9%
- ✓Incident response < 5 min
- ✓Mean time to recovery < 15 min
- ✓Zero unplanned restarts
Cost Efficiency
- ✓Cost per prediction < $0.01
- ✓Resource utilization > 60%
- ✓Spot instance % > 50%
- ✓No over-provisioning
Ready to Deploy?
Use this playbook as your deployment checklist. Build the infrastructure, implement the monitoring, practice the procedures.