What is Inference Monitoring Dashboard?

Success metrics include system uptime, model performance stability, deployment velocity, and operational cost efficiency.

Organize the dashboard into four sections: traffic overview (prediction volume per minute with historical comparison, request success/error rate breakdown, active model version distribution across endpoints), latency analysis (p50, p95, p99 latency time series with SLA threshold lines, latency breakdown by model version and input size category, geographic latency distribution if serving multiple regions), model quality (prediction confidence score distribution, output distribution comparison against training baseline, drift detection scores with alert status), and infrastructure health (GPU utilization and memory per serving instance, CPU and network throughput, autoscaling events and current replica count, cost-per-prediction trend). Use Grafana with Prometheus for metrics collection, or Datadog for managed monitoring. Include annotation support to mark deployment events, configuration changes, and incident periods on timeline charts.

Implement three-tier alerting with progressive escalation: informational alerts (Slack channel notifications for metrics trending toward thresholds: latency p95 above 70% of SLA, error rate above 50% of threshold, prediction drift score increasing), warning alerts (direct Slack message to on-call engineer for metrics breaching soft thresholds for 5+ minutes: latency SLA exceeded, error rate above 2%, significant prediction distribution shift), and critical alerts (PagerDuty page for metrics breaching hard thresholds: complete endpoint failure, error rate above 10%, model returning identical predictions for all inputs indicating corruption). Set maintenance windows suppressing alerts during planned deployments. Review alert frequency monthly: alerts triggering more than 3 times weekly without requiring action need threshold adjustment. Target fewer than 5 actionable alerts per week per model.

Organize the dashboard into four sections: traffic overview (prediction volume per minute with historical comparison, request success/error rate breakdown, active model version distribution across endpoints), latency analysis (p50, p95, p99 latency time series with SLA threshold lines, latency breakdown by model version and input size category, geographic latency distribution if serving multiple regions), model quality (prediction confidence score distribution, output distribution comparison against training baseline, drift detection scores with alert status), and infrastructure health (GPU utilization and memory per serving instance, CPU and network throughput, autoscaling events and current replica count, cost-per-prediction trend). Use Grafana with Prometheus for metrics collection, or Datadog for managed monitoring. Include annotation support to mark deployment events, configuration changes, and incident periods on timeline charts.

Implement three-tier alerting with progressive escalation: informational alerts (Slack channel notifications for metrics trending toward thresholds: latency p95 above 70% of SLA, error rate above 50% of threshold, prediction drift score increasing), warning alerts (direct Slack message to on-call engineer for metrics breaching soft thresholds for 5+ minutes: latency SLA exceeded, error rate above 2%, significant prediction distribution shift), and critical alerts (PagerDuty page for metrics breaching hard thresholds: complete endpoint failure, error rate above 10%, model returning identical predictions for all inputs indicating corruption). Set maintenance windows suppressing alerts during planned deployments. Review alert frequency monthly: alerts triggering more than 3 times weekly without requiring action need threshold adjustment. Target fewer than 5 actionable alerts per week per model.

Organize the dashboard into four sections: traffic overview (prediction volume per minute with historical comparison, request success/error rate breakdown, active model version distribution across endpoints), latency analysis (p50, p95, p99 latency time series with SLA threshold lines, latency breakdown by model version and input size category, geographic latency distribution if serving multiple regions), model quality (prediction confidence score distribution, output distribution comparison against training baseline, drift detection scores with alert status), and infrastructure health (GPU utilization and memory per serving instance, CPU and network throughput, autoscaling events and current replica count, cost-per-prediction trend). Use Grafana with Prometheus for metrics collection, or Datadog for managed monitoring. Include annotation support to mark deployment events, configuration changes, and incident periods on timeline charts.

Implement three-tier alerting with progressive escalation: informational alerts (Slack channel notifications for metrics trending toward thresholds: latency p95 above 70% of SLA, error rate above 50% of threshold, prediction drift score increasing), warning alerts (direct Slack message to on-call engineer for metrics breaching soft thresholds for 5+ minutes: latency SLA exceeded, error rate above 2%, significant prediction distribution shift), and critical alerts (PagerDuty page for metrics breaching hard thresholds: complete endpoint failure, error rate above 10%, model returning identical predictions for all inputs indicating corruption). Set maintenance windows suppressing alerts during planned deployments. Review alert frequency monthly: alerts triggering more than 3 times weekly without requiring action need threshold adjustment. Target fewer than 5 actionable alerts per week per model.