What is end-to-end ML observability stack design in production systems?

An end-to-end ML observability stack combines metrics, logs, traces, and data/model monitoring across the entire ML lifecycle.

An end-to-end ML observability system unifies monitoring across data pipelines, feature stores, training jobs, and inference services. It captures system metrics (CPU/GPU, latency), application logs (requests, errors), distributed traces (request flow across services), and ML-specific signals (drift, accuracy, calibration). The goal is not just detecting failure but enabling root-cause analysis across the entire pipeline. Modern stacks integrate Prometheus/Grafana for metrics, OpenTelemetry for tracing, and specialized ML monitoring tools for drift and performance degradation.