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OpenShift AI Platform Guide: Platform Engineering, GPUs, and Air-Gapped Clusters with OpenShift
OpenShift AI Platform Guide is a practical handbook for platform engineers who need to turn OpenShift into a real internal AI platform, not “just a Kubernetes cluster.”
Starting from the CNCF platform engineering whitepaper, the book shows how to apply those ideas on treating the platform as a product, reducing cognitive load for app teams, and building opinionated “golden paths” instead of one-off snowflakes.
From there, you’ll walk through end-to-end, production-grade
Installing OpenShift 4.20 in fully air-gapped environments with a local Quay registry
Configuring cluster-wide proxies, NFS storage, and disconnected OperatorHub catalogs
Deploying and managing key operators like Node Feature Discovery and the NVIDIA GPU Operator
Enabling InfiniBand and RDMA networking with SR-IOV and the NVIDIA Network Operator
Integrating observability with DCGM, Prometheus, and Grafana for GPU-aware monitoring
Using GitOps (OpenShift GitOps / Argo CD + GitLab) for declarative, auditable platform config
Running LLM performance benchmarks as code with Hugging Face’s Inference-Benchmarker and visualizing results with a Gradio dashboard
The guide is written in a “do this, then this” style, with YAML examples, command snippets, and explanations of why each piece matters for a modern AI platform.
If you are a platform engineer, SRE, or infrastructure-minded ML practitioner responsible for OpenShift-based GPU clusters—especially in regulated or disconnected environments—this book gives you a concrete, repeatable blueprint.
Starting from the CNCF platform engineering whitepaper, the book shows how to apply those ideas on treating the platform as a product, reducing cognitive load for app teams, and building opinionated “golden paths” instead of one-off snowflakes.
From there, you’ll walk through end-to-end, production-grade
Installing OpenShift 4.20 in fully air-gapped environments with a local Quay registry
Configuring cluster-wide proxies, NFS storage, and disconnected OperatorHub catalogs
Deploying and managing key operators like Node Feature Discovery and the NVIDIA GPU Operator
Enabling InfiniBand and RDMA networking with SR-IOV and the NVIDIA Network Operator
Integrating observability with DCGM, Prometheus, and Grafana for GPU-aware monitoring
Using GitOps (OpenShift GitOps / Argo CD + GitLab) for declarative, auditable platform config
Running LLM performance benchmarks as code with Hugging Face’s Inference-Benchmarker and visualizing results with a Gradio dashboard
The guide is written in a “do this, then this” style, with YAML examples, command snippets, and explanations of why each piece matters for a modern AI platform.
If you are a platform engineer, SRE, or infrastructure-minded ML practitioner responsible for OpenShift-based GPU clusters—especially in regulated or disconnected environments—this book gives you a concrete, repeatable blueprint.
361 pages, Kindle Edition
Published November 20, 2025
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