What rollback strategies can be applied when a deployed ML model fails?

Rollback strategies for a failed ML model deployment include: 1) **Immediate Rollback**: Quickly revert to the last stable version if performance declines sharply, minimizing impact. 2) **Shadow Rollback**: Route a portion of incoming traffic to the previous version while monitoring both versions, ensuring stability before full reversion. 3) **Blue-Green Deployment**: Maintain two environments (blue for the current version, green for the new version) to switch back seamlessly if needed. 4) **Feature Toggles**: Disable specific features of the new model that might cause issues while keeping it deployed.