Guides Overview
Key takeaway
The data and AI guides are best-practice playbooks covering strategy, data governance, platform architecture, MLOps, and the AI stack. They are written for leaders and delivery teams. Reading them in sequence helps teams align business goals first, then implement the operating model and technical foundations that make analytics and AI reliable.
The Guides section provides structured, best-practice playbooks for leaders and delivery teams.
Summary Card
Guides at a glance Strategy -> Governance -> Architecture -> MLOps -> AI Stack
Key point Use these guides as a sequence: align business goals first, then implement the operating model and technical foundations.
Featured Guides
- Data Governance and Stewardship
- Data Platform Architecture
- AI Risk and Governance
- MLOps and Operationalization
- AI Stack
Next Steps
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Frequently asked questions
In what order should I read the data and AI guides?
Read the guides as a sequence: strategy, then governance, then architecture, then MLOps, then the AI stack. Aligning business goals first ensures that the operating model and technical foundations you build later serve clear outcomes rather than becoming disconnected infrastructure or governance work.
Who are the data and AI guides written for?
The guides are written for both leaders and delivery teams. Leaders can use them to set priorities and operating models, while delivery teams can apply the practical checklists for governance, platform architecture, AI risk, MLOps, and the layered AI stack to day-to-day design and build decisions.
What topics do the data and AI guides cover?
The guides cover data governance and stewardship, data platform architecture, AI risk and governance, MLOps and operationalization, and the AI stack. Each pairs a short summary with actionable practices grounded in recognized frameworks such as DAMA-DMBOK, the NIST AI Risk Management Framework, and the AWS Well-Architected Framework.
