Description
This IBM Redbooks publication provides a comprehensive guide to evaluating, selecting, and implementing the right hybrid cloud AI deployment model for enterprise environments. The book covers the hybrid AI landscape and deployment model fundamentals, structured decision frameworks and customer readiness assessment, IBM Power and Spyre portfolio positioning, real-world use cases and business case development, hands-on implementation of agentic AI and RAG pipelines, infrastructure sizing and operational best practices, and governance, compliance, and advanced integration patterns. It is intended for network security architects, data architects, consultants, and senior technical professionals who need to make and justify defensible AI deployment decisions — particularly in regulated industries where data sovereignty, latency, and compliance requirements constrain architectural choices.
Starts 05 October 2026, ends 15 December 2026, and requires 8 residents
Residency Leader:HENRY VOLocation: Remote
Benefits to Resident
IBM Redbooks want technical practitioners to be recognized for their newly gained knowledge, experience, and accomplishments. Therefore, upon successful delivery of the publication produced by this IBM Redbooks residency, you will become eligible to earn an IBM Redbooks Digital Badge issued through Pearson VUE Acclaim.
Resident Prerequistes
A basic requirement for all residents is the ability to read and clearly express concepts and procedures in common English.
Skills
Skill | Level |
|---|---|
| Based on the outline — decision frameworks, IBM Power + Spyre, watsonx, RAG pipelines, agentic AI, regulatory compliance, and hybrid cloud architecture — here are the three top required resident skills: Hybrid Cloud and AI Architecture | 5 |
| Regulatory Compliance and Data Governance — Familiarity with data sovereignty frameworks and sector-specific regulations (GDPR, DORA, HIPAA) as they apply to AI workload placement, audit logging, model explainability, and enterprise compliance architecture. | 5 |
| AI Application Development and Integration — Hands-on experience building or integrating AI solutions — particularly RAG pipelines, LangChain-based agentic workflows, vector databases, and API gateway patterns — against enterprise systems such as IBM i, Db2, or equivalent platforms. | 5 |