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How do you monitor ML infrastructure cost?

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Answer

  • (1) Tag resources by (team, project, model, environment).
  • (2) Cost per model per day: infra cost + prediction volume → /prediction.(3)Alertsonunexpectedcostspikes(>2σ).(4)Budgetalertsperteam.(5)FinOpsdashboard:idleGPU,over−provisionedpods,expensive−but−unusedfeatures.(6)Right−sizingrecommendations:instancetypes+autoscalebounds.(7)Costperexperiment(trainingrun).Tools:AWSCostExplorer,GCPBilling,Kubecost.Businessimpactper/prediction. (3) Alerts on unexpected cost spikes (>2σ). (4) Budget alerts per team. (5) FinOps dashboard: idle GPU, over-provisioned pods, expensive-but-unused features. (6) Right-sizing recommendations: instance types + autoscale bounds. (7) Cost per experiment (training run). Tools: AWS Cost Explorer, GCP Billing, Kubecost. Business impact per spent = key metric.
Check yourself — multiple choice
  • Random
  • Tag by team/project/model/env + $/prediction per model + spike alerts (2σ) + budget alerts + FinOps dashboard (idle GPU/over-provision) + right-size + cost per experiment; AWS/GCP/Kubecost
  • Not needed
  • Just count

Cost monitoring: tag + $/prediction + spike alerts + FinOps + right-size.

#monitoring#infrastructure

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