Your data-science team runs its forecasting service in Kubernetes and exposes predictions through a REST endpoint /predict. You want to release updated model versions frequently while keeping latency below 50 ms for most requests. The release process must be able to:
direct only a small percentage of real-time traffic to the new version at first,
observe live accuracy and latency metrics before expanding use, and
roll back immediately if production quality degrades. Which deployment approach BEST satisfies these requirements and follows API-access best practices?
Mirror 100 % of live requests to the new model in a shadow deployment but discard its predictions so users never see them.
Update the existing /predict endpoint in-place and rely on automated container restarts to roll back if health checks fail.
Use a blue-green deployment that replaces all production pods with the new version during a scheduled maintenance window.
Configure an API-gateway canary release that routes a small, weighted percentage of /predict calls to the new model version and adjusts the weight based on monitored metrics.
A canary release sends a configurable fraction (for example, 1-10 %) of live requests through an API gateway or service mesh to the new model version while the existing version continues to handle the rest. Because traffic is split at the gateway level, you can collect real-world metrics without fully committing all users. If issues arise you simply reset the traffic weight to 0 %-an almost instantaneous rollback that avoids downtime. Blue-green swaps 100 % of traffic in a single cut-over, so it cannot observe the new model under partial load. Shadow deployments duplicate traffic but never serve their responses, so they do not validate end-user latency or accuracy. In-place updates expose every user to the new version at once and depend on container restarts to undo failures, which is slower and riskier.
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