FinOps & AI Technology Economics Bootcamp Certificate

Checkpoint 1: Ingest a realistic multi-account/cloud cost dataset; normalize dimensions, define ownership and create a reconciled baseline that ties engineering resources to spend.
Checkpoint 2: Implement allocation rules for tags, shared services and unallocated spend; reconcile totals, document rule ownership and produce showback/chargeback-ready views.
Checkpoint 3: Define at least two unit-economic metrics such as cost per customer, request, feature or transaction; build a dashboard that connects spend to usage and business output.
Checkpoint 4: Model optimization levers for commitments, rightsizing, storage/data and workload scheduling; distinguish realized savings from theoretical recommendations.
Checkpoint 5: Build an AI economics model covering tokens or GPU/inference consumption, model choice/routing, caching and quality/latency tradeoffs; compute cost per successful task.
Checkpoint 6: Forecast spend and variance, add anomaly detection and governance thresholds; inject a cost spike, investigate root cause and produce an engineering/finance decision memo.

Day 1: Define technology scopes, stakeholders and decision cadence; create a cost taxonomy covering cloud, data/platform and AI spend with explicit ownership and value questions.
Day 2: Normalize the billing dataset, map accounts/projects/products and allocate shared costs; prove monthly reconciliation and quantify the remaining unallocated percentage.
Day 3: Build unit-cost metrics and a decision dashboard; connect infrastructure/AI consumption to product usage, business volume and at least one outcome metric.
Day 4: Create optimization scenarios for rates and workloads; model savings, implementation effort, risk and forecast impact and separate recommendations from realized outcomes.
Day 5: Build an AI/token economics simulator comparing at least two model or serving configurations on cost, task quality, latency and capacity; identify the efficient frontier.
Day 6: Add forecast/budget variance, anomaly controls and an incident workflow; simulate runaway spend or demand change and document containment plus stakeholder communication.
Day 7: Publish cost model, normalized dataset sample, allocation logic, unit-economics dashboard, AI economics analysis, forecast, incident evidence and executive decision memo.