Method

Financial auditing for machine learning cost attribution

A calm sequence for turning opaque GPU and training invoices into owner-ready narratives.

Abstract network visualization suggesting data flows

The spine we teach

Attribution fails when teams jump straight to chargeback percentages. We start by inventorying what can be known, then design tags, then allocate only what remains shared.

  • 1

    Separate known from estimated

    Label every line as observed, derived, or assumed — and keep assumptions visible.

  • 2

    Bind owners early

    Map model families to budget owners before arguing about formula precision.

  • 3

    Document the mediation

    Shared pools need a signed rule, not a hallway agreement that vanishes at quarter-end.

Where this shows up in class

The flagship ML Spend Attribution Lab walks the full spine with paired exercises. Shorter intensives zoom into ledger reconciliation or inference billing alone.

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What “good enough” looks like

Perfect attribution is rare. A durable process names uncertainty, revisits rules on a cadence, and keeps finance and engineering in the same room when the estate changes shape.