rollouts

Rollouts refer to the staged release of a product, feature, or policy to users or environments rather than a full immediate deployment. The goal is to reduce risk, gather data, and ensure stability by exposing only a portion of users or systems to the changes at first.

Common strategies include phased rollouts, canary releases, blue-green deployments, and the use of feature flags. In a phased rollout, the update is gradually expanded to more users or regions. A canary release directs a small percentage of traffic to the new version to detect issues. Blue-green deployment maintains two identical production environments and switches traffic to the updated one when ready. Feature flags allow hidden or toggled activation, enabling dynamic control without redeploying.

Planning usually involves selecting a rollout scope, defining success criteria, and establishing telemetry and rollback plans. Metrics can include error rates, latency, user impact, and adoption. If problems are detected, operators can halt progression, roll back to the previous version, or patch the issue.

In product management and marketing, rollouts may also describe staged introductions, campaign rollouts, or geographic expansion, accompanied by customer support and localization considerations.

In reinforcement learning and some AI contexts, a rollout denotes a trajectory produced by executing a policy in the environment, used for evaluation, planning, or training. This is a different sense from deployment rollouts, though the term shares the idea of extending behavior or exposure.

Benefits of rollouts include improved risk management, feedback collection, and faster recovery from failures. Potential downsides include longer time to full availability, complexity in coordinating multiple environments, and insufficient data if the rollout is too small or poorly monitored.