AI & Data
Best Practice: Implement model rollback mechanisms for safe deployments
Sep 12, 2024
Deploying AI models into production environments can introduce unforeseen issues, such as performance degradation or operational failures. Safe deployment requires robust rollback mechanisms to revert to previous versions when needed. These mechanisms ensure smooth recovery in case of errors, minimising disruption and protecting business operations.
Why Model Rollback Mechanisms Matter
- Quick recovery from failures: If a newly deployed model underperforms or fails, rollback mechanisms allow teams to revert to the last stable version quickly, preventing extended downtime or suboptimal results.
- Mitigating risks in production: Rolling out models directly to full production environments can expose the system to unexpected risks. A rollback plan mitigates these risks by providing a safe path back if issues arise.
- Improved trust in model deployment: Teams can confidently deploy new models knowing that if something goes wrong, there is a simple and reliable way to recover to a known good state.
- Reducing business impact: Rollback mechanisms help minimise negative impacts on customers and business operations by allowing seamless recovery, especially in high-stakes or mission-critical systems.
Implementing This Best Practice
- Use canary deployments: Canary deployment strategies involve deploying the new model to a small subset of users or a limited portion of the traffic. This allows for performance testing under real conditions before the model is rolled out more widely. If issues arise, the new version can be easily replaced with the previous one.
- Blue-green deployments: With blue-green deployments, the new model is deployed alongside the existing one in parallel environments. Traffic is gradually switched over to the new model, and if problems occur, it can quickly be switched back to the old model without downtime.
- Automate rollback procedures: Automate the rollback process as part of the deployment pipeline. This ensures that if performance issues are detected, the system can immediately revert to the previous model version without manual intervention.
- Monitor key performance indicators (KPIs): Set up real-time monitoring to track key metrics, such as accuracy, latency, and throughput, during deployment. If metrics fall below acceptable thresholds, trigger an automatic rollback.
Conclusion
Implementing model rollback mechanisms is essential for safe AI deployments. Canary and blue-green deployment strategies, combined with automated rollback procedures, provide a structured way to test and revert models, reducing risks and ensuring operational continuity. This practice enhances trust in AI systems and minimises potential disruptions.
