Release management controls how and when features become available to users, which differs from deployment itself. While deployment moves code into production, release management determines when that code affects user experience. The distinction matters because conflating the two creates unnecessary risk. Understanding different release strategies helps teams navigate this complexity. Deployment is not release Traditional […]
Deploying machine learning models breaks most assumptions about continuous delivery. Unlike a typical API or web service, an ML model can degrade without any code changes. Your pipeline passes all tests, but the model starts making worse predictions because the data distribution shifted. This creates problems that traditional CD workflows weren’t designed to handle. The […]
Trunk-based development requires developers to merge code into the main branch frequently, often multiple times per day. This creates a problem: how do you keep unfinished features from reaching users while maintaining a releasable main branch? Progressive delivery workflows solve this through feature flags, which separate code deployment from feature release. Decoupling deployment and release […]
Most A/B testing happens at the frontend layer, where product teams change button colors, adjust copy, or reorganize layouts. These experiments work well because the surface area is small and the behavior is deterministic, making it easy to predict outcomes. AI-powered features don’t fit this model. A recommendation algorithm might behave differently based on training […]
