What is ML Disaster Recovery?

Implement comprehensive monitoring, automated testing, version control, incident response procedures, and continuous improvement processes aligned with organizational objectives.

ML disaster recovery adds four unique requirements: model artifact recovery (versioned model binaries, weights, and configuration stored in geographically redundant storage with 99.999% durability, recoverable within 15 minutes), training data backup (immutable snapshots of training datasets stored separately from processing infrastructure, enabling model retraining from scratch if needed), feature pipeline reconstruction (documented and automated pipeline definitions that can recreate feature stores from raw data sources within hours), and model serving state recovery (cached predictions, warm model instances, and routing configurations restorable without cold-start degradation). Define Recovery Time Objective (RTO) per model based on business criticality: under 15 minutes for revenue-critical models, under 2 hours for important models, under 24 hours for non-critical models. Test recovery procedures quarterly through simulated disaster exercises.

Deploy a three-tier DR architecture: active-active for critical models (serve from two regions simultaneously with traffic routing via global load balancer, achieving near-zero RTO but 2x cost), warm standby for important models (maintain model artifacts and pre-configured infrastructure in the backup region, achieving 15-30 minute RTO at 30% additional cost), and cold standby for non-critical models (store artifacts in cross-region replicated storage with infrastructure-as-code definitions for rapid provisioning, achieving 2-4 hour RTO at 10% additional cost). Automate failover using health check-triggered DNS switching or cloud provider failover services. Synchronize model registries across regions to ensure the backup region always has the latest production model versions. Test failover monthly for active-active systems and quarterly for warm/cold standby configurations.

ML disaster recovery adds four unique requirements: model artifact recovery (versioned model binaries, weights, and configuration stored in geographically redundant storage with 99.999% durability, recoverable within 15 minutes), training data backup (immutable snapshots of training datasets stored separately from processing infrastructure, enabling model retraining from scratch if needed), feature pipeline reconstruction (documented and automated pipeline definitions that can recreate feature stores from raw data sources within hours), and model serving state recovery (cached predictions, warm model instances, and routing configurations restorable without cold-start degradation). Define Recovery Time Objective (RTO) per model based on business criticality: under 15 minutes for revenue-critical models, under 2 hours for important models, under 24 hours for non-critical models. Test recovery procedures quarterly through simulated disaster exercises.

Deploy a three-tier DR architecture: active-active for critical models (serve from two regions simultaneously with traffic routing via global load balancer, achieving near-zero RTO but 2x cost), warm standby for important models (maintain model artifacts and pre-configured infrastructure in the backup region, achieving 15-30 minute RTO at 30% additional cost), and cold standby for non-critical models (store artifacts in cross-region replicated storage with infrastructure-as-code definitions for rapid provisioning, achieving 2-4 hour RTO at 10% additional cost). Automate failover using health check-triggered DNS switching or cloud provider failover services. Synchronize model registries across regions to ensure the backup region always has the latest production model versions. Test failover monthly for active-active systems and quarterly for warm/cold standby configurations.

ML disaster recovery adds four unique requirements: model artifact recovery (versioned model binaries, weights, and configuration stored in geographically redundant storage with 99.999% durability, recoverable within 15 minutes), training data backup (immutable snapshots of training datasets stored separately from processing infrastructure, enabling model retraining from scratch if needed), feature pipeline reconstruction (documented and automated pipeline definitions that can recreate feature stores from raw data sources within hours), and model serving state recovery (cached predictions, warm model instances, and routing configurations restorable without cold-start degradation). Define Recovery Time Objective (RTO) per model based on business criticality: under 15 minutes for revenue-critical models, under 2 hours for important models, under 24 hours for non-critical models. Test recovery procedures quarterly through simulated disaster exercises.

Deploy a three-tier DR architecture: active-active for critical models (serve from two regions simultaneously with traffic routing via global load balancer, achieving near-zero RTO but 2x cost), warm standby for important models (maintain model artifacts and pre-configured infrastructure in the backup region, achieving 15-30 minute RTO at 30% additional cost), and cold standby for non-critical models (store artifacts in cross-region replicated storage with infrastructure-as-code definitions for rapid provisioning, achieving 2-4 hour RTO at 10% additional cost). Automate failover using health check-triggered DNS switching or cloud provider failover services. Synchronize model registries across regions to ensure the backup region always has the latest production model versions. Test failover monthly for active-active systems and quarterly for warm/cold standby configurations.