inference-rollout-strategist

Choose by intent: canary mitigates risk on a small traffic slice for routine promotions, blue-green gives instant cutover and rollback via a full parallel variant, and A/B runs a fixed split for weeks to gather statistical evidence on which model wins. Start at shadow for the first exposure of a high-risk model.

Shadow deployment mirrors live production requests to the new model version while serving the incumbent's response and discarding the shadow response after logging. It validates the candidate on the true production distribution with zero user risk before any traffic is served.

Use a bandit when a losing variant is costly and you want to stop paying for it mid-experiment. A bandit dynamically shifts traffic toward the winner using exploration strategies like epsilon-greedy or Thompson sampling, while a fixed A/B keeps sending traffic to losers for the whole run.

Use asymmetric autoscaling: fast scale-out with a near-zero stabilization window and slow scale-in with a long window such as 120 seconds or more. Drive KEDA or HPA from Prometheus serving metrics like request rate and p95 latency rather than node CPU, and preload model weights from a cache so new replicas start in seconds.

Wire rollback to sustained quality or safety regressions below the gate floor, candidate-correlated p95 latency or error-rate breaches, and confirmed drift signals, each with a documented confirmation window and incumbent comparison. Define the trigger, automated action, and pager target before shifting the first percent of traffic.

Yes, the plan targets KServe-class serving with weighted revisions for traffic splitting, KEDA/HPA plus Karpenter for scaling, and Prometheus for driving metrics. It is designed for Lambda-class managed Kubernetes environments serving GPU inference workloads.