How do you design useful ML dashboards?

medium

Answer

$(1) Top-of-funnel: at-a-glance health (traffic + errors + latency + drift).$
$(2) Drill-down per model / feature / slice.$
$(3) One 'page' per concern (drift, accuracy, infra, cost).$
$(4) Comparison: current vs baseline / last week.$
$(5) Annotations for deploys / incidents.$
$(6) Alerts embedded (fires on this graph).$
$(7) Links to runbooks.$
$(8) Own audience: engineer dashboards \neq exec dashboards.$
$Bad dashboard: 40 charts nobody reads; good: 5 charts driving action.$

Check yourself — multiple choice

More charts
Top-funnel health + drill-down per model + one-page-per-concern + baseline comparison + deploy annotations + embedded alerts + runbook links + audience-appropriate; 5 actionable > 40 unused
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Dashboards: layered + comparative + annotated + audience-aware.

#observability

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