Configuration errors are a leading cause of large-scale outages. This article highlights how Meta uses automated canarying, ML-driven alerting, and a blameless culture to maintain system stability while scaling deployment speed in an AI-accelerated environment.
As AI increases developer speed and productivity it also increases the need for safeguards.
On this episode of the Meta Tech Podcast, Pascal Hartig sits down with Ishwari and Joe from Meta’s Configurations team to discuss how Meta makes config rollouts safe at scale. Listen in to learn about canarying and progressive rollouts, the health checks and monitoring signals used to catch regressions early, and how incident reviews focus on improving systems rather than blaming people.
They also talk about how data and AI/machine learning are slashing alert noise and speeding up bisecting when something goes wrong.
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The post Trust But Canary: Configuration Safety at Scale appeared first on Engineering at Meta.
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