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Practical RHEL AI Designing, Deploying and Scaling AI Solutions with Red Hat Enterprise Linux (Luca Berton)（Z-Library）

Luca Berton

Practical RHEL AI Designing, Deploying and Scaling AI Solutions with Red Hat Enterprise Linux (Luca Berton)（Z-Library）

Author Luca Berton

Linux

If you're looking to build, deploy, and scale AI solutions with confidence, Practical RHEL AI is the guide you need. Whether you're an AI developer, data scientist, or DevOps engineer, this book walks you through the entire process—from setting up your AI development environment to optimizing and securing enterprise-scale AI workloads on Red Hat Enterprise Linux. You'll start with the essentials: installation, configuration, and leveraging powerful machine learning frameworks like TensorFlow, PyTorch, and Scikit-learn. Then, you’ll dive into the tools that make AI deployment seamless—GPU acceleration, containerization, and cloud integration with AWS and Azure. Security and compliance are non-negotiable in AI, and this book makes sure you get them right. Learn how to protect your models with encryption, implement role-based access control (RBAC), and meet industry standards like GDPR and HIPAA. You’ll also master AI workload monitoring with Prometheus and Grafana, troubleshoot common issues, and automate deployments with Ansible. However, theory only gets you so far—real-world applications make the difference. Through hands-on examples and case studies in healthcare, finance, and manufacturing, you’ll see how RHEL AI powers innovation in the field. Plus, you'll get insights into the future of AI, including Explainable AI (XAI), Edge AI, and AI governance. With Practical RHEL AI, you’re not just learning AI—you’re building AI solutions that scale. This Book is for: AI and machine learning engineers, DevOps and system administrators, Data scientists, and IT professionals and cloud architects

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Practical RHEL AI Designing, Deploying and Scaling AI Solutions with Red Hat Enterprise Linux — Luca Berton
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Practical RHEL AI Designing, Deploying and Scaling AI Solutions with Red Hat Enterprise Linux Luca Berton
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Practical RHEL AI: Designing, Deploying and Scaling AI Solutions with Red Hat Enterprise Linux ISBN-13 (pbk): 979-8-8688-1900-1 ISBN-13 (electronic): 979-8-8688-1901-8 https://doi.org/10.1007/979-8-8688-1901-8 Copyright © 2026 by Luca Berton This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. Trademarked names, logos, and images may appear in this book. Rather than use a trademark symbol with every occurrence of a trademarked name, logo, or image we use the names, logos, and images only in an editorial fashion and to the benefit of the trademark owner, with no intention of infringement of the trademark. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein. Managing Director, Apress Media LLC: Welmoed Spahr Acquisitions Editor: Divya Modi Editorial Assistant: Gryffin Winkler Cover designed by eStudioCalamar Cover image designed by Pixabay Distributed to the book trade worldwide by Springer Science+Business Media New York, 1 New York Plaza, New York, NY 10004. Phone 1-800-SPRINGER, fax (201) 348-4505, e-mail orders-ny@springer-sbm.com, or visit www.springeronline.com. Apress Media, LLC is a Delaware LLC and the sole member (owner) is Springer Science + Business Media Finance Inc (SSBM Finance Inc). SSBM Finance Inc is a Delaware corporation. For information on translations, please e-mail booktranslations@springernature.com; for reprint, paperback, or audio rights, please e-mail bookpermissions@springernature.com. Apress titles may be purchased in bulk for academic, corporate, or promotional use. eBook versions and licenses are also available for most titles. For more information, reference our Print and eBook Bulk Sales web page at http://www.apress.com/bulk-sales. Any source code or other supplementary material referenced by the author in this book is available to readers on GitHub. For more detailed information, please visit https://github.com/Apress/ Practical-RHEL-AI. If disposing of this product, please recycle the paper Luca Berton Amsterdam, The Netherlands
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For my son Filippo, the joy of my life.
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xi About the Author Luca Berton is a seasoned AI automation and DevOps expert with more than 18 years of experience in IT, specializing in cloud infrastructure, machine learning platforms, and enterprise-scale automation. He has led major AI and automation initiatives for financial institutions such as JPMorgan Chase, Société Générale, ABN Amro, and BPCE, designing GPU-accelerated Kubernetes/OpenShift AI clusters and optimizing CI/CD pipelines for regulated environments. Luca is the creator of the popular Ansible Pilot project and author of several best-selling technical books, including Ansible for Kubernetes by Example and Hands-on Ansible Automation. A former Red Hat engineer, he has made significant contributions to the open-source ecosystem, particularly in enhancing Ansible's capabilities for cloud and AI workloads. Widely recognized for his teaching and community leadership, Luca regularly shares his expertise through courses on Coursera, Pluralsight, and Educative and speaks at global tech conferences on topics ranging from MLOps to infrastructure automation.
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xiii About the Technical Reviewer Kenneth Hitchcock currently works as a senior architect with over 25 years of experience in IT. He has spent the last 15 years predominantly focused on Red Hat products, certificating himself as a Red Hat Architect along the way. Originally from Durban, South Africa, he now lives in England, where he hopes to not only continue inspiring all he meets but also to continue improving himself and the industry he works in.
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xv Acknowledgments To my son, family, and friends, who make life worth living and whose support and encouragement make this work possible. To all whom I’ve worked with over the years and shared any ideas for this book. Thank you for the knowledge you’ve shared.
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xvii Introduction 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. This book, Practical RHEL AI, is your field guide to doing precisely that—securely, repeatably, and at scale. RHEL AI 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. I draw on 18 years of experience automating highly regulated environments at JPMorgan Chase & Co., Société Générale, and BPCE. If you've watched one of my Ansible screencasts, you already know my style: pragmatic, vendor-neutral, and allergic to hand-waving. My mission with this book is simple—hand you a reproducible recipe for building AI services that auditors, SREs, and CFOs will all sign off on. 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
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xviii If you are an AI engineer, DevOps lead, or architect tasked with “making GenAI real,” keep this book next to your terminal. Each chapter includes runnable examples, so you can translate theory into commits the same day. How This Book Is Structured Chapter 1: Introduction to RHEL AI We begin with the why: the business drivers behind RHEL AI and the architectural pillars that distinguish it from roll-your-own Python stacks—security, reproducibility, and hybrid cloud reach. You will spin up your first RHEL AI image and tour the built-in InstructLab CLI that powers the rest of the book. Chapter 2: Setting Up RHEL AI Next comes groundwork. Hardware sizing tables help you pick between A100, H100, or MI300X GPUs; Kickstart snippets automate bare metal installs; and cloud templates launch ready-to-train images on AWS, Azure, or GCP. By the chapter’s end, you will have a GPU node that passes the ILAB system info with flying colors. Chapter 3: Exploring Core Components Here, we dissect the “four-step” InstructLab workflow—crafting YAML skills, generating synthetic data, fine-tuning, and serving models—and see how cache folders, taxonomy trees, and model registries fit together. You will walk away understanding exactly where every checkpoint and JSONL file lives on disk. Chapter 4: Advanced Features of RHEL AI Performance junkies, rejoice. DeepSpeed ZeRO-3, MiCS communication scaling, FP8 inference, and NVMe offload are demystified with benchmark tables that show real numbers on H100 and MI300X silicon. Flip a single flag in your ds_config.json and watch the time-to-train drop by 40%. Chapter 5: Developing Custom AI Applications Turning models into revenue means aligning them with domain knowledge. This chapter teaches you to write a one-page capability statement, translate it into taxonomy seeds, and run the curate → generate → train → serve loop. End-to-end examples cover underwriting classification and multilingual chatbots, complete with Ansible playbooks for CI. InTroduCTIon
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xix Chapter 6: Monitoring and Maintenance AI without observability is a ticking time bomb. You will wire GPU thermals, cgroup pressure, vLLM latency buckets, and MMLU drift scores into a single Grafana lens, then define SLOs that map directly to SLIs like P95 ≤ 80 ms. The result is an on-call playbook your SREs will actually trust. Chapter 7: Use Cases and Best Practices From retrieval-augmented generation to edge-deployed sentiment analysis, we distill field patterns into decision tables: when to retrain vs. when to retrieve, which vector store to pick, and how to inject policy-as-code gates. Each pattern includes Terraform or OpenShift manifests you can fork on Friday afternoon. Chapter 8: Future Trends in RHEL AI Regulations tighten, GPUs evolve, and explainability moves from “nice to have” to a contract clause. This forward-looking chapter forecasts attribution pipelines baked into the ilab model evaluate, SPDX lineage for every weight, and carbon-aware scheduling. You will leave with a road map that keeps your platform ahead of audits and silicon curves alike. Chapter 9: Community and Support Finally, we plug you into Red Hat’s lifeline: enterprise SLAs, weekly InstructLab Discord calls, community taxonomies, and contributor sprints. Learn how to open a support ticket, file a pull request, or present your custom skill at the next summit—and never feel alone in production again. Ready to Build AI That Ships? 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 that sounds like your reality, crack open your terminal, provision a GPU, and turn to Chapter 1. Practical RHEL AI is about to make enterprise AI boring—in the best possible way—so your team can focus on delivering features that matter. Let’s build something remarkable. InTroduCTIon
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1 © Luca Berton 2026 L. Berton, Practical RHEL AI, https://doi.org/10.1007/979-8-8688-1901-8_1 CHAPTER 1 Introduction to RHEL AI Artificial intelligence is reshaping the digital landscape, and enterprises are eager to adopt AI technologies that are powerful but in a secure, scalable, and compliant way. This chapter introduces RHEL AI—a platform built on the reliable Red Hat Enterprise Linux foundation—to streamline AI development and deployment in enterprise environments. We explore the motivations behind RHEL AI, its architecture, key capabilities, and provide step-by-step guidance for setting up our first AI-enabled system. By the end of this chapter, you will understand how RHEL AI empowers organizations to operationalize AI initiatives with confidence. AI in Enterprise Artificial intelligence (AI) is no longer confined to academic research or experimental applications; it has become an essential component of enterprise information technology. Whether facilitating predictive analytics, real-time decision-making, process automation, or customer engagement, AI is driving transformative change across various industries. Nevertheless, the development of AI systems that are secure, scalable, maintainable, and compliant with enterprise standards continues to present significant challenges. Red Hat Enterprise Linux (RHEL) AI exemplifies a pinnacle of trust and innovation, providing a unified platform to develop, deploy, and manage intelligent applications. It leverages the established reliability of RHEL, coupled with integrated AI tools, frameworks, and best practices, to operationalize machine learning (ML) and deep learning (DL) within a modern IT infrastructure. This chapter explores the strategic importance of RHEL AI, its fundamental architecture, detailed installation procedures, and methods of integration within real-world enterprise environments.
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2 Red Hat Following the inception of Linux in 1991, Red Hat’s journey commenced in 1993 with its founding, driven by an ambitious objective: to introduce open-source software to the global community. During an era dominated by proprietary systems, Red Hat prioritized collaboration, transparency, and community engagement. This vision materialized with the release of Red Hat Linux in 1995, one of the pioneering Linux distributions to attain commercial success while adhering to open-source principles. A significant milestone was reached in 2002 with the focus on the enterprise market through the launch of Red Hat Enterprise Linux (RHEL). It introduced a subscription- based model integrating open-source software with enterprise-level support, thereby establishing a foundation for open-source software within corporate information technology environments. RHEL swiftly established itself as a benchmark for reliability, security, and lifecycle management, paving the way for future innovations by Red Hat. In subsequent years, Red Hat expanded its scope beyond Linux to encompass hybrid cloud, virtualization, and containerization. The acquisition of JBoss in 2006 augmented its application platform portfolio, and the 2014 introduction of OpenShift, Red Hat’s Kubernetes-based container platform, positioned the company as a leader in cloud-native computing. These milestones exemplify Red Hat’s consistent ability to anticipate technological trends and to align open-source communities with business and enterprise needs. Red Hat’s commitment to open source extended beyond product development. The company became one of the foremost corporate contributors to the Linux kernel and other pivotal projects such as Ansible, Ceph, Fedora, and Podman. Through governance, funding, and support for developers, Red Hat contributed to the vitality of the global open-source community while ensuring that innovation remained accessible. In 2019, IBM acquired Red Hat for $34 billion, marking a landmark event in the technology sector. The acquisition did not signify a departure from Red Hat’s open- source principles but was instead a strategic initiative to enhance its influence in cloud computing and artificial intelligence technologies. IBM was committed to maintaining Red Hat’s independence and open model, allowing it to persist as a neutral leader within the broader ecosystem. This partnership amplified Red Hat’s engagement in artificial intelligence, particularly through collaborative efforts in open hybrid cloud infrastructure, AI workloads, and foundational model development. Chapter 1 IntroduCtIon to rheL aI
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3 Presently, Red Hat continues to serve as a cornerstone of the open-source movement. It has effectively navigated decades of technological transformations without compromising its fundamental values. From its nascent Linux distributions to AI-compatible platforms, its contributions have shaped the way open-source software is developed, adopted, and trusted—empowering a global community of developers and enterprises to innovate collaboratively. Why RHEL AI In the AI landscape, companies face pressure to innovate while ensuring security, scalability, and reliability. Red Hat’s focus on AI grew with the 2023 launch of Red Hat Enterprise Linux AI (RHEL AI), its first dedicated AI product. It offers a bootable container optimized for AI development, training, and inference, with tools like InstructLab, DeepSpeed, and vLLM, plus immediate GPU acceleration. This move expands Red Hat’s open-source AI efforts, enabling developers to build and deploy large language models on transparent infrastructure. Since May 2025, Red Hat has also launched the llm-d project, a Kubernetes-native large language model inference framework. RHEL AI combines AI capabilities with Red Hat’s reliable foundation, focusing on confident, efficient, and enterprise-grade model creation. A primary reason to select RHEL AI is its emphasis on security. Red Hat’s long- standing reputation for delivering secure and reliable infrastructure extends to its AI solutions. RHEL AI includes features such as SELinux-based access control, regular security updates, container integrity checks, and components designed for compliance. For organizations operating within regulated industries or managing sensitive data, this level of security is not optional; it is essential. Performance also constitutes a vital aspect of the RHEL AI experience. Since version 1.4 of RHEL AI, it is specifically engineered to support demanding AI workloads, particularly large-scale model training and inference. It supports NVIDIA GPUs, including the latest A100 and H100 series, as of the current writing, and integrates robust AI frameworks such as DeepSpeed, FSDP, and vLLM. This configuration enables developers and data scientists to attain exceptional speed and efficiency when training or deploying large language models (LLMs), supported by hardware acceleration and software optimization. Chapter 1 IntroduCtIon to rheL aI
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4 A noteworthy feature of RHEL AI is the inclusion of InstructLab, a versatile AI toolkit that simplifies the customization and deployment of LLMs. With the built-in ilab command-line interface, users can create synthetic data, develop skill taxonomies, train models on custom data, and serve these models for real-time inference or chat- based applications. InstructLab streamlines complex AI tasks into manageable steps, facilitating rapid iteration and scalable deployment. RHEL AI also demonstrates flexibility in deployment. Whether operating on bare metal servers, private data centers, or across major public cloud providers, RHEL AI is designed to be platform-independent. It supports Amazon Web Services, Microsoft Azure, Google Cloud Platform, and IBM Cloud, enabling organizations to deploy once and scale efficiently. This hybrid and multicloud approach ensures that AI infrastructure integrates seamlessly with existing architectures, rather than necessitating complete redesigns. Red Hat’s strength lies in its enterprise support system, with tools for automation, monitoring, and lifecycle management under RHEL AI. Automation, proactive monitoring, and dedicated support streamline operations. The open-source ethos combines community innovation with enterprise stability, fostering confident innovation. RHEL AI is impacting diverse sectors with AI applications like chatbots, virtual assistants, and analytics, promoting faster, trustworthy development. Choosing RHEL AI signifies a move toward responsible, scalable AI, enabling smarter innovation, quicker deployment, and better security in a growing AI landscape. Use Cases The true impact of Red Hat Enterprise Linux AI (RHEL AI) becomes evident when its robust features are employed to address practical challenges across diverse industries. By facilitating advanced analytics, intelligent decision-making, and automation, RHEL AI assists organizations in developing tailored AI solutions that not only satisfy complex business requirements but also uphold enterprise standards concerning security, scalability, and compliance. In the healthcare industry, RHEL AI is being used to improve how medical professionals interact with information. Large language models trained on private clinical data allow organizations to create assistants that can summarize complex medical documents and provide intelligent triage support for patient symptoms. These models respect privacy and compliance requirements while enhancing both the speed and accuracy of clinical workflows. Chapter 1 IntroduCtIon to rheL aI
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