Deliver Results, Not Just Releases: Control & Observability in CD
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How do companies like Netflix, LinkedIn, and booking.com crush it year after year? Yes, they release early and often. But they also build control and observability into their CD pipeline to turn releases into results. Progressive delivery and the statistical observation of real users (sometimes known as “shift right testing” or “feature experimentation”) are essential CD practices. They free teams to move fast, control risk and focus engineering cycles on work that delivers results, not just releases. Learn implementation strategies and best practices for adding control and observability to your CD pipeline: Where should you implement progressive delivery controls: front-end or back-end? Why balancing centralization/consistency and local team autonomy in your implementation will increase the odds of achieving results you can trust and observations your teams will act upon. What two pieces of data make it possible to attribute system and user behavior changes to any deployment? How “guardrail” metrics can automate observability of unintended consequences of deployments, without adding overhead to teams making changes or tasking your exploratory testers and data scientists to go looking for them. This talk is from our partner
Transcript
How do companies like Netflix, LinkedIn, and booking.com crush it year after year? Yes, they release early and often. But they also build control and observability into their CD pipeline to turn releases into results.
Progressive delivery and the statistical observation of real users (sometimes known as “shift right testing” or “feature experimentation”) are essential CD practices. They free teams to move fast, control risk and focus engineering cycles on work that delivers results, not just releases.
Learn implementation strategies and best practices for adding control and observability to your CD pipeline: Where should you implement progressive delivery controls: front-end or back-end? Why balancing centralization/consistency and local team autonomy in your implementation will increase the odds of achieving results you can trust and observations your teams will act upon. What two pieces of data make it possible to attribute system and user behavior changes to any deployment? How “guardrail” metrics can automate observability of unintended consequences of deployments, without adding overhead to teams making changes or tasking your exploratory testers and data scientists to go looking for them.
This talk is from our partner
About the speakers
Continuous Delivery Evangelist at Split