Turns out “a collection of random thoughts” is not the most compelling way to onboard someone into AI and OpenShift. Who knew. This book pulls together the most useful posts from this blog, reorganizes them into something that actually flows. Same content, less chaos.
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Abstract:
A practical, hands-on guide to building and operating your own AI stack, from lightweight homelab setups to production-ready platforms. This book includes specific code examples and walks through real architectures, covering local AI setups, deploying cloud-based platforms, exposing AI inference endpoints, and working with AI agents.

If you want to move beyond using AI APIs and start owning the full stack, infrastructure, models, and workflows, this guide shows you how to get there step by step.

Built from real-world experiments and field experience, it is designed for engineers and architects who prefer learning by doing. It serves as a practitioner’s companion for hands-on exploration. The examples are intended for learning and proof-of-concept work and are not production-ready; proper consideration of security, safety, and resiliency is essential before real-world deployment.