Practical RHEL AI
Designing, deploying & scaling GenAI on Red Hat Enterprise Linux AI
A field guide and book to shipping enterprise GenAI with Red Hat Enterprise Linux AI (RHEL AI). Learn the full InstructLab workflow, serve models with vLLM, and run them like production services. Fine-tune on Friday, deploy to production on Monday — securely, repeatably, and at scale.
Who This Book Is For
For engineers who need AI that ships — and survives security reviews, audits, and production on-call.
DevOps & Linux Engineers
You run RHEL fleets and want a disciplined path to deploy GenAI workloads without “Python snowflakes.”
Platform Engineers
You’re building internal platforms and need repeatable patterns for inference services, monitoring, and rollout safety.
AI / MLOps Teams
You fine-tune models, but you want a production-ready workflow: synthetic data, evaluation, serving, telemetry, and drift.
Architects & Tech Leads
You need an honest, practical blueprint to evaluate RHEL AI and deploy enterprise GenAI with governance in mind.
What You’ll Build
Practical workflows you can implement the same day — from model customization to production operations.
Practical RHEL AI vs. Typical GenAI Tutorials
Most guides teach you to get something working on your laptop. This book teaches you to ship and operate it.
Typical GenAI Tutorials
- "Works on my machine"
- Single-node, ad-hoc setup
- Jupyter notebooks + Python scripts
- Demo accuracy, not production readiness
- No monitoring or alerting
- Weak governance and auditability
- One-off fine-tuning, no versioning
- Undocumented or fragile deployments
Practical RHEL AI
- Repeatable builds across dev, stage, prod
- Fleet-ready deployments with Ansible and systemd
- Taxonomy-driven fine-tuning with InstructLab
- Synthetic data pipelines for continuous training
- Production-grade monitoring: GPU telemetry, latency SLOs, drift detection
- Enterprise security: RBAC, audit logs, compliance
- Model versioning & rollback strategies
- Opinionated, battle-tested patterns from real deployments
Chapter Map
Your learning journey from foundations to production mastery
Companion Resources
Code, examples, and supporting material referenced in the book.
GitHub Companion Repo
Grab the source code and supplementary material from the official Apress repository.
Sample / Media Kit
Want a sample chapter, slides, or a workshop outline? Request it by email.
Frequently Asked Questions
Quick answers before you buy.
Do I need prior ML/AI experience?
Not necessarily. The book assumes you’re comfortable with Linux, containers, and basic automation. AI concepts are introduced as you build real workflows.
Does it cover InstructLab and synthetic data workflows?
Yes — you’ll learn the full workflow: taxonomy-driven skills, synthetic data generation, fine-tuning, evaluation, and serving.
Do I need a GPU?
You can start learning without one, but training and performance tuning sections benefit from GPU hardware. The book also covers operational considerations like GPU telemetry.
Is this focused on production, not just demos?
Yes. Monitoring, maintenance, reliability goals, and best practices are core topics — not an afterthought.
Where can I get the companion code?
The official companion repository is available on GitHub under the Apress organization.
Does this book cover Ansible playbooks for deployment?
Yes. The companion code includes repeatable, production-ready Ansible playbooks for deploying RHEL AI across your infrastructure.
Can I run RHEL AI in air-gapped or regulated environments?
Yes. The book covers strategies for container-based deployments, offline model loading, and compliance-ready practices for regulated industries.
Is RHEL AI the same as OpenShift AI?
No. RHEL AI is the base platform for running GenAI workloads on Red Hat Enterprise Linux. OpenShift AI is a higher-level, container-orchestrated variant. This book focuses on RHEL AI core concepts, which also apply to OpenShift AI deployments.
Does it cover GPU acceleration and optimization?
Yes. Chapters on GPU telemetry, CUDA optimization, memory management, and multi-GPU serving with vLLM are all included.
What's the focus on model evaluation and drift detection?
Production operations require drift detection and continuous evaluation. The book covers synthetic test set generation, automated quality checks, and monitoring dashboards to track model performance over time.
GitHub Companion Repo
Full source code and working examples
End-to-End vLLM + InstructLab
Production-ready model serving and fine-tuning
Production Checklists
Rollout safety, monitoring, drift detection, RBAC
Ready to Build AI That Ships?
Practical RHEL AI is hands-on, opinionated, and built for engineers who care about uptime, security, and debuggability.