Feature flags deliver the control needed to decouple deploy from release but can break traditional monitoring and KPIs. The good news is that teams are using them to "kill the release night" by moving from big-bang releases to gradual releases during normal business hours. The bad news is that gradual release techniques add challenges to traditional ways of monitoring system health and user behavior. No one wants to move faster if that means less visibility and thus greater risk.
We'll look at advanced implementation techniques that marry the precision control of feature flags with automated ingest of data and statistical computation of KPIs. This allows teams to proactively identify system performance and user behavior differences between the status quo and new code. Advanced feature flagging implementations “build-in” observability to every release. When you push a feature to 5% of users, it becomes trivial to see how user and system behavior varies for those users vs. the other 95%. Teams further along this journey auto-calculate “do-no-harm" metrics, so it’s easy to detect unintended consequences of their work before ramping up to all users.
You’ll leave this session with a clear vision of how your team can achieve the same benefits, by either enhancing your in-house solution or adopting a commercial tool.