Infrastructure Monitoring for ML Systems - Interactive Visualization

Production ML systems require continuous monitoring of infrastructure metrics: CPU and GPU utilization, request latency (p99 matters most), and error rate. Prometheus scrapes /metrics endpoints every 15 seconds; Grafana renders PromQL queries into dashboards; Alertmanager routes alerts to PagerDuty or Slack based on severity. AWS CloudWatch provides the same capability for AWS-hosted workloads. Standard SLOs: p99 latency under 500ms, error rate under 1%, CPU under 80%, GPU under 90%. This demo shows live metric sparklines and automatic alert firing with one-click resolve.

• Prometheus scrapes /metrics every 15s; uses TSDB for efficient time-series storage with 15-day retention by default
• Alertmanager deduplicates, groups, and routes alerts - prevents alert storms from flooding on-call engineers
• p99 latency threshold: above 500ms typically triggers warning; above 1s triggers page for ML inference services
• GPU utilization monitoring: consistently above 90% means the node is bottlenecked; below 30% means waste
• Error rate spikes before latency spikes - error rate is the fastest leading indicator of a degraded model endpoint
• CloudWatch Logs Insights + Metrics: AWS-native alternative to Prometheus, tightly integrated with SageMaker endpoints

Part of the EngineersOfAI Interactive 3D - free interactive visualizations covering every major concept in machine learning and AI engineering. Hover any element for a plain-English explanation. No code required.