Site Reliability Engineering

SRE bridges the traditional gap between development and operations by having engineers who write software to manage production systems. Key practices include defining Service Level Objectives that quantify reliability targets, using error budgets to balance reliability with feature velocity, automating repetitive operational tasks, and conducting blameless postmortems to learn from incidents.

For AI product teams, SRE practices are essential because AI systems have unique reliability challenges: model drift causes silent degradation, data pipeline failures produce stale features, and non-deterministic model behavior complicates incident diagnosis. SRE teams define SLOs for AI services that include both availability and quality metrics, such as the recommendation service must return results within 200ms for 99.5% of requests, with relevance scores above a threshold. Growth teams benefit from SRE practices because reliable infrastructure is the foundation of trustworthy experiment results. If production instability introduces noise into experiment metrics, it becomes impossible to detect real treatment effects, wasting both user traffic and engineering time.