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OpenShift AI production & cost readiness

:::info Flagship thesis (hypothesis) A larger direction that individual validated ideas can roll up into. Still a hypothesis — start with the named first wedge, not the whole platform. :::

Purpose​

Provide a premium enterprise lane while SME ideas are validated. Do not recreate native platform capabilities without a specific gap.

Proposed workflow
Text alternative (accessible description)
  1. Platform and data-flow evidence leads to Security and governance.
  2. Platform and data-flow evidence leads to Evaluation and reliability.
  3. Platform and data-flow evidence leads to Token, API and GPU cost.
  4. Security and governance leads to Risk register.
  5. Evaluation and reliability leads to Risk register.
  6. Token, API and GPU cost leads to Risk register.
  7. Risk register leads to Target architecture.
  8. Target architecture leads to 90-day implementation backlog.
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  1. Platform and data-flow evidence leads to Security and governance.
  2. Platform and data-flow evidence leads to Evaluation and reliability.
  3. Platform and data-flow evidence leads to Token, API and GPU cost.
  4. Security and governance leads to Risk register.
  5. Evaluation and reliability leads to Risk register.
  6. Token, API and GPU cost leads to Risk register.
  7. Risk register leads to Target architecture.
  8. Target architecture leads to 90-day implementation backlog.

Step 1: Platform and data-flow evidence leads to Security and governance.

Deliverables​

Current-state assessment, risk register, capacity and cost model, target architecture, governance baseline, implementation backlog, and optional pilot.