The pipeline you "trust" isn’t failing the way you expect. Problems arrive as late data, a slow transform step, config drift in a dependency, or a flurry of transient infra faults that cascade into silent model degradation. Those symptoms look like intermittent failures, longer tail latencies, or stalled runs; they become outages because your instrumentation either never existed or was too noisy to act on. The payoff from surgical telemetry and crisp alerts is faster detection, fewer escalations, and shorter time‑to‑recover — not more complex dashboards. Contents Why the Four Golden Signals Are the Fastest Way to Detect ML Pipeline Regressions How to Instrument Pipelines: Metrics, Logs, and Distributed Traces Designing Alerts, SLOs, and Effective Escalation Policies Dashboards That Let You See Regressions Before Users Do Postmortem Workflow and Reducing Time-to-Recover Practical Application Sources Why the Four Golden Signals Are the Fastest Way to Detect ML Pipeline Regressions The canonical SRE golden…