In a webinar hosted by The Top Voices, Software Engineer Sergey Sidorov explained how to accelerate change delivery without compromising reliability — using progressive rollout strategies and built-in safety mechanisms. The session is titled How to Apply Canary Releases in Practice with Lessons from Real-World Deployments.
Speaker
Sergey Sidorov is a Software Engineer at Meta, working on the change safety infrastructure and internal reliability systems. His work includes SLICK, a platform for early regression detection and production stability.
The Risk of Fast Change
Modern engineering teams ship hundreds of updates daily, increasing the likelihood of failures. Even small changes can lead to outages due to hidden dependencies. Major incidents in recent years — including global outages at Meta and CrowdStrike, and a halt in trading at the New York Stock Exchange — illustrate how the absence of staged rollouts, monitoring, and automated rollback mechanisms can amplify impact.
Deployment ≠ Release
To reduce risk, it’s essential to separate deployment (code delivery) from release (user exposure). This is achieved through feature flags, which control when and for whom a feature is active. But feature flags alone don’t prevent technical regressions. Canary releases — gradual rollout to a small user segment with health checks — add the necessary safeguards.
Progressive Rollout and Automation
Rollouts start with a small subset of users and expand only if system and business metrics remain healthy. Automated gates can pause or revert changes when issues arise. At Meta, this is implemented via change safety systems tightly integrated with via software deployment tool Conveyor and the rest of the ecosystem including deployment pipelines, config systems, service mesh and traffic routing, and monitoring infrastructure. Conveyor enables precise rollout strategies and automatic rollback for about 97% deployments
Scalability of the Approach
Even without in-house infrastructure, teams can adopt progressive delivery practices: use percentage-based feature flags, predefine metrics, and prepare rollback plans. Cloud providers like AWS and GCP offer native support for this, and open-source tools like Spinnaker, Argo Rollouts, and Flagger provide additional options. All types of changes — code or config — should follow the same safety process.
Conclusion
Progressive rollout is not a slowdown — it’s the foundation of sustainable velocity. It reduces incident frequency, builds confidence, and enables safe experimentation. This approach scales well and is accessible to teams at any maturity level.