ML Engineering6 min read•January 17, 2026

Shipping ML Models With Rollback Plans

Best practices for deploying ML models with confidence and quick rollback capabilities.

LL
Written byLuis LapoFounder at Data Systems Academy. Focused on production data systems and ML engineering.
Tags
ml-engineeringdeploymentreliability

Why rollback plans matter

ML models can fail in production in ways code never does. A model that worked perfectly in testing can degrade due to data drift, concept drift, or edge cases.

Deployment patterns

Blue-green deployment: Keep previous model version running. Switch traffic gradually.

Canary deployment: Route small percentage of traffic to new model. Monitor metrics closely.

Shadow mode: Run new model alongside old one without affecting predictions. Compare outputs.

Pre-deployment checks

Before deploying:

  1. Performance validation: Model meets accuracy thresholds on holdout set
  2. Latency checks: Inference time is within SLA
  3. Resource validation: Model fits in memory and doesn’t overload infrastructure
  4. A/B test setup: Framework ready to compare old vs. new model

Monitoring for rollback triggers

Set up alerts for:

  • Prediction quality: Accuracy drops below threshold
  • Business metrics: Revenue, engagement, or other KPIs degrade
  • Error rates: Inference failures or exceptions spike
  • Data drift: Input distribution shifts significantly

The rollback procedure

  1. Automated rollback: Trigger on critical alerts
  2. Manual rollback: One-command revert to previous version
  3. Post-mortem: Document what went wrong and why
  4. Model retraining: Fix issues and redeploy with lessons learned