ai observability

A practical, Linux-native guide to AI observability: why ML fails differently, how to stand up Prometheus, Grafana, and Jaeger with apt/dnf/zypper, expose model-aware metrics and OpenTelemetry traces, and reproduce four real cases—feature drift, P95 latency spikes, RAG hallucinations, and silent data breakage—with copy-paste commands, dashboards, and alert rules to cut detection and debug time.

As AI moves into production, trust hinges on observability: live, model-aware signals (p95 latency, tokens, drift, hallucination risk) tied to SLOs and cost. This article delivers a Linux-first, vendor-neutral recipe using Prometheus/Grafana, a Python exporter, dashboards, alerts, and privacy/cost guardrails, plus a look ahead to standards, request-level tracing, GPU/eBPF telemetry, and self-tuning SLOs.

Practical, Bash-first guide to AI observability on Linux: why ML needs more than CPU/HTTP metrics, how to define AI-specific SLIs/SLOs, emit JSON logs, push Prometheus metrics via Pushgateway, build Grafana dashboards, deploy canary/shadow releases, track drift, and wire alerts for latency, errors, cost, and safety—with copy-paste installs, scripts, PromQL, and an end-to-end test.

Pragmatic, bash-first guide to AI observability on Linux: in ~30 minutes set up Prometheus + Node Exporter for host metrics, a lightweight Python nvidia-smi GPU exporter, and app-level Prometheus metrics; then use eBPF/bpftrace one-liners for deep dives, apply ready-made PromQL for triage, and finish with fixes and next steps (Grafana, alerts, tracing) - no Kubernetes required, apt/dnf/zypper cmds included.