🚀 Master Enterprise AI on Red Hat Enterprise Linux! 🚀
Artificial intelligence is no longer a Research and Development side project—it is the foundational system of modern business. Whether automating claims processing, discovering new medicines, or defending cloud borders in real time, AI determines who wins the next decade.
Yet, most enterprises still struggle with the same frustrations: toolchains that break between development and production, GPU clusters that behave like cats on a hot tin roof, and governance officers who appear the day after a model goes astray.
Red Hat Enterprise Linux AI (RHEL AI) was built to turn that chaos into disciplined velocity, allowing you to fine-tune models on Friday and ship them to production on Monday.
📖 “Practical RHEL AI: Designing, Deploying and Scaling AI Solutions with Red Hat Enterprise Linux” combines the rock-solid pedigree of Red Hat Enterprise Linux with an opinionated AI stack that integrates DeepSpeed, vLLM, InstructLab, and GPU drivers, allowing you to spend time designing solutions, not searching for the right CUDA wheel.
With it, a small team can stand up a private ChatGPT clone, wire it into Grafana for drift alerts, and sleep soundly knowing SELinux is still on duty.
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By the final page, you will be able to:
✅ Install and harden a GPU-accelerated RHEL AI cluster in any hybrid cloud
✅ Generate synthetic data, fine-tune Granite or Mixtral models, and serve them through an OpenAI-compatible API
✅ Automate monitoring, drift detection, and CI/CD so models evolve without surprise regressions
✅ Map cutting-edge trends—Explainable AI, Edge AI, AI governance—to concrete RHEL AI features you can deploy next quarter
This comprehensive guide is designed for:
✅ Install, Configure, and Scale AI models with a comprehensive guide to RHEL AI
✅ Secure, Optimize, and Comply with enterprise-ready AI solutions for large-scale environments
✅ Integrate and Accelerate AI workloads using cloud services (AWS, Azure) and GPU optimization
✅ Apply and Implement hands-on examples and real-world use cases in healthcare, finance, and manufacturing
✅ Monitor and Troubleshoot AI performance with Prometheus, Grafana, and automated maintenance tools
✅ Production-Ready Workflows from development to enterprise deployment
✅ InstructLab Integration for fine-tuning Granite and Mixtral models
✅ Security-First Approach with SELinux, encryption, and compliance frameworks
• Install and Configure RHEL AI to optimize machine learning workloads
• Train and Deploy AI models using TensorFlow, PyTorch, Scikit-learn, and InstructLab
• Integrate and Implement GPU acceleration, cloud computing, and containerization for scalable AI solutions
• Secure and Evaluate AI workloads with encryption, RBAC, and compliance best practices
• Monitor and Troubleshoot AI performance using Prometheus and Grafana
• Automate AI workflows with Ansible and CI/CD pipelines
• Implement Explainable AI, Edge AI, and AI governance frameworks
Luca Berton is an experienced Ansible Automation expert with over 18 years in IT, specializing in DevOps, Cloud Engineering, and System Administration. He has written several best-selling books, including “Ansible for VMware by Example,” “Ansible for Kubernetes by Example,” “Hands-on Ansible Automation,” “Red Hat Ansible Automation,” and “Mastering the Red Hat Certified Engineer (RHCE) Exam.”
Luca draws on 18 years of experience automating highly regulated environments at JPMorgan Chase & Co., Société Générale, and BPCE. He is the creator of the Ansible Pilot project and has made significant contributions to the open-source community, particularly in improving Ansible’s functionality. Luca has been acknowledged for his active involvement in conferences and contributions to the Red Hat community through various events and publications.
The pages ahead are opinionated, hands-on, and battle-tested. They assume you would rather see a snippet than a slide, and that uptime, security, and debuggability matter as much as model quality.
If you are an AI engineer, DevOps Lead, or Architect tasked with “making GenAI real,” this book is your field guide to doing precisely that—securely, repeatably, and at scale.
Pre-order today and master enterprise AI deployment on Red Hat Enterprise Linux!
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