incident-triage

You can triage Kubernetes ML inference incidents by correlating time-pinned Prometheus signals and deployment changes. This approach uses Grafana's anonymous proxy and read-only Gerrit lookups to find the failing layer and identify causal commits without cluster access.

Prometheus metrics for unavailable replicas and pod crash loops are retrieved via range-based PromQL queries around a precise incident timestamp. These queries target deploy, readiness, pod, and mesh error flags across ML-serve namespaces to isolate the failing layer.

Correlating Gerrit merges with inference-services error-rate spikes involves querying Gerrit for matching commits in inference-services and deployment-charts within a time window. This identifies likely causal commits and distinguishes multi-CL roll forwards from reactive changes.

Incident triage for LiftWing requires UTC-precise timestamp pinning with year, month, day, hour, minute, and timezone. This exact timestamp anchors the range-based PromQL queries and deployment-change context needed to produce a structured diagnosis.

The best way to structure root cause analysis reports for Kubernetes inference-services alerts is a consistent format containing Date, Issue description, Root cause with confidence, and Potential solutions, alongside a current state recovery and regression check.

Your LiftWing inference error-rate spike triage might miss the actual deployment change if the timestamp is not UTC-precise or the default ±2 hour window fails to capture the causal commit, preventing accurate correlation of Prometheus signals with Gerrit merges.