The Ultimate Linux & Cloud Infrastructure Bundle by Packt

This is an outdated edition, and we have a new third edition live covering real-world patterns, GenAI strategies, cost optimization techniques, and certification-aligned best practices. Key Features Comprehensive guide to automating, networking, migrating, and adopting cloud technologies using AWS Extensive insights into AWS technologies, including AI/ML, IoT, big data, blockchain, and quantum computing to transform your business. Detailed coverage of AWS solutions architecture and the latest AWS certification requirements Book DescriptionThe second edition of AWS for Solutions Architects provides a practical guide to designing cloud solutions that align with industry best practices. This updated edition covers the AWS Well-Architected Framework, core design principles, and cloud-native patterns to help you build secure, high-performance, and cost-effective architectures. Gain a deep understanding of AWS networking, hybrid cloud connectivity, and edge deployments. Explore big data processing with EMR, Glue, Kinesis, and MSK, enabling you to extract valuable insights from data efficiently. New chapters introduce CloudOps, machine learning, IoT, and blockchain, equipping you with the knowledge to develop modern cloud solutions. Learn how to optimize AWS storage, implement containerization strategies, and design scalable data lakes. Whether working on simple configurations or complex enterprise architectures, this guide provides the expertise needed to solve real-world cloud challenges and build reliable, high-performing AWS solutions.What you will learn Optimize your Cloud Workload using the AWS Well-Architected Framework Learn methods to migrate your workload using the AWS Cloud Adoption Framework Apply cloud automation at various layers of application workload to increase efficiency Build a landing zone in AWS and hybrid cloud setups with deep networking techniques Select reference architectures for business scenarios, like data lakes, containers, and serverless apps Apply emerging technologies in your architecture, including AI/ML, IoT and blockchain Who this book is for This book is for application and enterprise architects, developers, and operations engineers who want to become well versed with AWS architectural patterns, best practices, and advanced techniques to build scalable, secure, highly available, highly tolerant, and cost-effective solutions in the cloud. Existing AWS users are bound to learn the most, but it will also help those curious about how leveraging AWS can benefit their organization. Prior knowledge of any computing language is not needed, and there’s little to no code. Prior experience in software architecture design will prove helpful.

A practical, hands-on guide to mastering cloud DevOps using Git, Docker, Terraform, Kubernetes, CI/CD, and AI-powered automation to build secure, scalable, production-ready systems. Key Features Build cloud DevOps CI/CD pipelines using GitHub Actions, Terraform, Kubernetes, and Helm Apply DevSecOps, observability, and cloud security best practices in real projects Explore GitOps, FinOps, AIOps, and modern career paths for cloud DevOps engineers Book DescriptionModern software delivery demands speed, reliability, and scalability, and cloud DevOps engineering makes it possible. The Cloud DevOps Engineer’s Guide is a practical, hands-on DevOps guide for building production-grade workflows using modern cloud-native tools and platforms. Starting with core DevOps and cloud concepts, this book helps you set up your workstation, master Git and Docker, and provision infrastructure using Terraform. You’ll build complete CI/CD pipelines with GitHub Actions, automate cloud deployments, and orchestrate containerized applications with Kubernetes and Helm. As you progress, you’ll implement DevSecOps practices, integrate monitoring, logging, and observability, and secure cloud environments using IAM, secrets management, and network controls. Advanced chapters cover reliability engineering, cost optimization, GitOps, FinOps, platform engineering, and AIOps. Packed with hands-on labs, real-world workflows, and automation-driven examples, this cloud DevOps engineer guide prepares you to run modern cloud systems with confidence and grow your career in an AI-powered cloud era.What you will learn Build a DevOps workstation with Git, Bash, Docker, and cloud CLIs Apply GitFlow branching, PR reviews, and merge conflict resolution Create reusable Terraform modules and manage remote state Implement CI tests and CD release workflows for staging/production Deploy to Kubernetes using manifests, Deployments, and Helm charts Enforce least-privilege IAM and manage secrets + encryption Define SLOs/SLIs, set alerting, and practice incident response runbooks Who this book is for This book is for aspiring and early-career DevOps engineers, cloud engineers, SREs, and software developers looking to transition into DevOps roles. It’s also ideal for system administrators, IT professionals, and freelancers who want hands-on experience with cloud-native DevOps tools, CI/CD pipelines, and modern automation practices.

Discover how AIOps is transforming the observability landscape for cloud-native and traditional systems. Learn how to build, monitor, and operate resilient services using AI-drive dynamic insights for smarter and more scalable operations Key Features Bridges observability and AI into a unified operational approach rather than treating them as separate domains Uses a continuous case study to connect concepts across chapters and reflect real-world engineering scenarios Focuses on evolving operational maturity from reactive to proactive and preventive systems Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionObservability is mandatory for building and operating cloud-native distributed systems. Tools like OpenTelemetry have standardized how observability data is sourced, and AI now transforms how we extract value from the vast amounts of observability data generated by modern systems. This book guides you in implementing scalable observability, improving engineering efficiency with AI, and integrating observability throughout the Software Development Lifecycle (SDLC) via modern self-service internal developer platforms. You'll start with observability basics and learn how AIOps enhances signal correlation, anomaly detection, and root-cause analysis. Using real-world examples, the book demonstrates how to implement AIOps, build proactive detection pipelines, and automate diagnostics and remediation. You'll explore best practices for expanding observability using OpenTelemetry, Prometheus, Grafana, Dynatrace, Datadog, and New Relic alongside machine learning models, ensuring your systems are accurate, efficient, and secure. You'll also learn how to benchmark, measure, and secure your AIOps implementation, and gain a practical understanding of software compliance and how it applies to your systems. By the end of this book, you'll be ready to design and deliver AIOps-enabled observability solutions that make cloud-native systems more resilient, efficient, and secure.What you will learn Build observability pipelines for logs, metrics, traces and events Implement standards such as OpenTelemetry and Prometheus Correlate signals from multiple sources for better incident triage Apply AI/ML for anomaly detection and root cause analysis Design scalable architectures for intelligent monitoring Automate resiliency through self-healing and remediation agents Who this book is for This book is for Software engineers and engineering leaders working on teams with operational responsibilities, such as platform engineering, site reliability engineering (SRE), DevOps, or application development, who want to integrate AIOps capabilities into their workflows will benefit from this book. If your team is responsible for building and running high-performing, resilient software systems, this book is for you.

Build AI-assisted network troubleshooting workflows that parse CLI output, call approved tools, use MCP, and keep evidence visible for review Key Features Build local LLM workflows for NetOps using Python, Ollama, and validated CLI data Create troubleshooting agents that use memory, approved tools, and clear evidence Package reusable network tools with MCP and plan controlled read-only pilots Book DescriptionNetwork troubleshooting is full of clues, but they are often buried in noisy alerts, long CLI output, missing topology context, and incomplete handoffs. Building AI Agents for Network Operations shows how to use AI agents, LLMs, and network automation in a controlled way, so engineers can get clearer evidence without giving up validation or operational control. You will start with local LLM workflows using Ollama and Python, then use a simple RACE prompt structure to make repeatable NetOps tasks clearer, safer, and easier to review. You will parse interface and BGP output into structured data, build a chatbot that keeps troubleshooting context, and connect the model to approved tools for device status, interfaces, reachability, topology, and BGP health. You will then build an agentic troubleshooting loop, package reusable network tools with MCP, and learn how to evaluate these workflows against logging, approvals, observability, runbooks, feature flags, and read-only pilot readiness. By the end of this book, you will have a practical path for turning AI ideas into NetOps workflows that can be tested in a lab, reviewed by your team, and adapted toward real-world network operations with the right controls.What you will learn Run local LLM workflows with Ollama and Python Shape reliable NetOps prompts using RACE Parse CLI and BGP output into structured JSON Build chatbots that remember troubleshooting context Connect AI agents to approved network tools Create evidence-based troubleshooting loops Expose reusable network tools with MCP Plan read-only pilots with safety controls Who this book is for This book is for network engineers, NetOps engineers, NOC engineers, SREs, DevOps engineers, and network automation professionals who want to apply AI to troubleshooting without losing control. Basic networking and CLI familiarity will help, beginner Python knowledge is useful for following the labs.

Decode the NVIDIA GPU ecosystem in one structured guide. Compare technologies, understand how platform layers interact, and build the judgment to evaluate infrastructure choices and trade-offs. Key Features Understand how GPUs, CUDA, networking, storage, and DPUs support AI workloads Learn the roles of MIG, vGPU, DCGM, Kubernetes, Slurm, NGC, and Triton Connect infrastructure components across the AI development and deployment lifecycle Book DescriptionNVIDIA GPU infrastructure spans hardware, system software, networking, storage, orchestration, MLOps, and inference. Understanding how these components fit together, where their responsibilities overlap, and which distinctions matter requires a clear, structured path. This book provides that path through one coherent narrative of the NVIDIA GPU infrastructure stack. It covers accelerated computing, CUDA, and the NVIDIA software ecosystem before comparing data center GPUs against workload characteristics. You will examine MIG, vGPU, and DCGM for resource sharing and monitoring; Kubernetes and Slurm for GPU scheduling; and the networking and storage layer, including Ethernet, InfiniBand, RDMA, GPUDirect Storage, BlueField DPUs, and DOCA. Later chapters connect infrastructure to the AI lifecycle through Airflow, MLflow, and Kubeflow for MLOps, NGC for software delivery, and ONNX, TensorRT, and Triton for inference. You will also explore the Kubernetes components, monitoring technologies, scaling considerations, and diagnostic concepts that support production GPU clusters. By connecting these technologies instead of presenting them as isolated products, the book helps you compare platform choices, understand component boundaries, discuss trade-offs, and develop a durable mental model of NVIDIA GPU infrastructure. What you will learn Distinguish AI, machine learning, and deep learning Explain why GPUs accelerate modern AI workloads Match NVIDIA GPUs to training and inference requirements Select MIG or vGPU for common resource-sharing scenarios Compare Ethernet and InfiniBand for distributed AI workloads Map MLOps tools to the right stage of the AI lifecycle Differentiate ONNX, TensorRT, and Triton in inference workflows Trace GPU cluster issues across platform layers Who this book is for This book is for system administrators, cloud and DevOps professionals, data center and networking teams, solution architects, technical managers, presales professionals, and beginners who need a clear understanding of NVIDIA GPU infrastructure. It is especially relevant to professionals moving into AI infrastructure roles, evaluating GPU platform technologies, or collaborating across compute, networking, MLOps, and operations teams. Basic familiarity with IT, cloud, or data center concepts is helpful; programming, data science, and previous GPU experience are not required.

Master Linux Shells – Your Complete Guide to Practical Success with Bash, Zsh, PowerShell Key Features Develop portable scripts using Bash, Zsh, and PowerShell that work seamlessly across Linux, macOS, and Unix systems Progress seamlessly through chapters with clear concepts, practical examples, and hands-on labs for skill development Build real-world Linux administration scripts, enhancing your troubleshooting and management skills Book DescriptionDive into the world of Linux shell scripting with this hands-on guide. If you’re comfortable using the command line on Unix or Linux but haven’t fully explored Bash, this book is for you. It’s designed for programmers familiar with languages like Python, JavaScript, or PHP who want to make the most of shell scripting. This isn’t just another theory-heavy book—you’ll learn by doing. Each chapter builds on the last, taking you from shell basics to writing practical scripts that solve real-world problems. With nearly a hundred interactive labs, you’ll gain hands-on experience in automation, system administration, and troubleshooting. While Bash is the primary focus, you'll also get a look at Z Shell and PowerShell, expanding your skills and adaptability. From mastering command redirection and pipelines to writing scripts that work across different Unix-like systems, this book equips you for real-world Linux challenges. By the end, you'll be equipped to write efficient shell scripts that streamline your workflow and improve system automation.What you will learn Grasp the concept of shells and explore their diverse types for varied system interactions Master redirection, pipes, and compound commands for efficient shell operations Leverage text stream filters within scripts for dynamic data manipulation Harness functions and build libraries to create modular and reusable shell scripts Explore the basic programming constructs that apply to all programming languages Engineer portable shell scripts, ensuring compatibility across diverse platforms beyond Linux Who this book is for This book is for programmers who use the command line on Unix and Linux servers already, but don't write primarily in Bash. This book is ideal for programmers who've been using a scripting language such as Python, JavaScript or PHP, and would like to understand and use Bash more effectively. It’s also great for beginning programmers, who want to learn programming concepts.

Master the complete Generative AI project lifecycle on Kubernetes (K8s) from design and optimization to deployment using best practices, cost-effective strategies, and real-world examples. Key Features Build and deploy your first Generative AI workload on Kubernetes with confidence Learn to optimize costly resources such as GPUs using fractional allocation, Spot Instances, and automation Gain hands-on insights into observability, infrastructure automation, and scaling Generative AI workloads Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionGenerative AI (GenAI) is revolutionizing industries, from chatbots to recommendation engines to content creation, but deploying these systems at scale poses significant challenges in infrastructure, scalability, security, and cost management. This book is your practical guide to designing, optimizing, and deploying GenAI workloads with Kubernetes (K8s) the leading container orchestration platform trusted by AI pioneers. Whether you're working with large language models, transformer systems, or other GenAI applications, this book helps you confidently take projects from concept to production. You’ll get to grips with foundational concepts in machine learning and GenAI, understanding how to align projects with business goals and KPIs. From there, you'll set up Kubernetes clusters in the cloud, deploy your first workload, and build a solid infrastructure. But your learning doesn't stop at deployment. The chapters highlight essential strategies for scaling GenAI workloads in production, covering model optimization, workflow automation, scaling, GPU efficiency, observability, security, and resilience. By the end of this book, you’ll be fully equipped to confidently design and deploy scalable, secure, resilient, and cost-effective GenAI solutions on Kubernetes.What you will learn Explore GenAI deployment stack, agents, RAG, and model fine-tuning Implement HPA, VPA, and Karpenter for efficient autoscaling Optimize GPU usage with fractional allocation, MIG, and MPS setups Reduce cloud costs and monitor spending with Kubecost tools Secure GenAI workloads with RBAC, encryption, and service meshes Monitor system health and performance using Prometheus and Grafana Ensure high availability and disaster recovery for GenAI systems Automate GenAI pipelines for continuous integration and delivery Who this book is for This book is for solutions architects, product managers, engineering leads, DevOps teams, GenAI developers, and AI engineers. It's also suitable for students and academics learning about GenAI, Kubernetes, and cloud-native technologies. A basic understanding of cloud computing and AI concepts is needed, but no prior knowledge of Kubernetes is required.

Learn to build, manage, and deploy containers on Linux using Podman, Buildah, and Skopeo, and integrate them with Kubernetes and systemd, while running AI models locally through the Podman Desktop interface Key Features Create containers from scratch using Buildah and move images securely with Skopeo Harden your container environment with rootless Podman, SELinux, and signed images Manage Kubernetes resources and streamline AI workflows with Podman Desktop Book Description Containers are transforming how modern applications are built, deployed, and scaled. Podman offers a powerful, secure alternative to Docker by eliminating the daemon and embracing rootless container execution. If you're ready to move beyond legacy workflows and gain full control over container management, this practical guide is for you. You’ll begin with container fundamentals and a side-by-side comparison of Docker and Podman to ease the transition. Then, you'll run your first container, manage its lifecycle, and use Buildah to build images from scratch. Skopeo helps you transfer and inspect images across registries. As you progress, you’ll secure your environment with rootless containers, signed images, and SELinux policies. You’ll also configure container networking, integrate workloads with systemd services, and troubleshoot issues using native Linux tools. The final chapters focus on modern developer workflows, showing how to migrate existing Docker workloads, manage containers and Kubernetes resources visually using Podman Desktop, and leverage Podman AI Lab to experiment with, run, and manage AI/ML models locally in a containerized environment. By the end of this book, you’ll be able to build, run, and secure containers, automate workflows, and confidently manage deployments across DevOps and AI-powered environments. What you will learn Understand the fundamentals and history of container technology Compare Podman and Docker to choose the right container engine Run and manage containers on various Linux environments using Podman Build containers from scratch using Buildah and manage them with Skopeo Secure containers using rootless execution, SELinux, and image signing Troubleshoot and monitor containers using system tools and Podman CLI Connect containers with advanced networking and integrate them with systemd Manage containers and explore AI/ML use cases with Podman Desktop Who this book is for The book is for cloud developers looking to learn how to build and package applications inside containers and system administrators who want to deploy, manage, and integrate them with system services and orchestration solutions. This book provides a detailed comparison between Docker and Podman to aid you in learning Podman quickly. Basic Linux skills are assumed. Familiarity with Docker, container concepts, and cloud environments is helpful but not required, as concepts are introduced progressively.

Transform your network operations with AI-powered automation, and learn code generation, prompt engineering, and practical recipes for building custom network tools using AI assistants and Python Key Features Leverage AI assistants like OpenAI and Claude to build network automation solutions Use prompt engineering and AI tools to automate network setup, monitoring, and threat detection Build AI-assisted network configuration, monitoring, and management workflows with multi-vendor APIs Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionTransform your approach to network automation with the power of AI LLM assistants guided by hands-on recipes for building custom automation solutions quickly using artificial intelligence. You’ll learn tools and techniques such as Vibe coding for conversational development, OpenAI API scripts, prompt engineering for better outputs, local LLM fine-tuning, combining models with LangChain, and Streamlit-based frontends development. The book progresses from simple Python scripts to advanced AI-assisted automation techniques, including multi-vendor API integration, showing you how AI can enhance network configuration, monitoring, security, and troubleshooting. Each recipe presents realistic mock data, complete code examples, and step-by-step guidance, creating a safe environment for experimentation while building a solid foundation for future production use. Whether you want to automate routine configuration, implement AI-driven troubleshooting, or build compliance monitoring systems, this cookbook helps you connect your networking expertise with the capabilities of modern AI.What you will learn Understand the AI LLM landscape and key parameters for networking tasks Create OpenAI-enabled scripts for daily network engineering workflows Master prompt engineering techniques for improved AI outputs Build local LLMs using Ollama for network applications Chain language models with LangChain for complex network solutions Develop AI application frontends using the Streamlit framework Design robust backends for network AI applications Build an end-to-end network copilot by integrating all the techniques you've learned Who this book is for The AI for Networking Cookbook is for experienced network engineers, network architects, and DevOps professionals who want to enhance their network automation capabilities using AI and LLM technologies. It is especially invaluable for networking professionals looking to integrate conversational AI development, prompt engineering, and modern AI tools like OpenAI APIs, LangChain, and local LLM models into their workflows. Familiarity with basic networking concepts, configurations, and Python is helpful, but no prior AI or advanced programming experience is required.

Gain the expertise to deploy, secure, and optimize Microsoft Entra ID while strengthening identity governance, protecting human and machine identities from modern threats, and enabling secure access to internet-based and private resources Key Features Implement identity governance, PIM, lifecycle workflows, and Zero Trust access Secure external, workload, and decentralized identities across hybrid enterprises Learn from a Microsoft Entra Principal Product Manager driving platform innovation Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionIdentity is the new front door to the organization's resources. Mastering Microsoft Entra ID provides a structured, end-to-end journey to help you design, secure, and govern enterprise identity with confidence. You will build a strong foundation in identity architecture, deploying tenants, managing users and groups, and configuring Conditional Access. From there, you will automate lifecycle workflows, manage privileged roles with PIM, and enforce least privilege at scale. As your expertise deepens, you will explore identity protection, workload identities, Global Secure Access, and practical migration strategies for modernizing on-premises environments. Written by a Microsoft Entra Principal Product Manager, this guide blends strategic clarity with real-world implementation insight to help you secure enterprise identities and confidently lead identity transformation across hybrid and cloud environments.What you will learn Apply Zero Trust principles to identity architecture Deploy Microsoft Entra tenants, roles, MFA, and Conditional Access Leverage Microsoft Security Copilot and Entra agents for identity operations Govern access using access packages, access reviews, and PIM Automate joiner, mover, and leaver identity lifecycles Investigate and remediate identity-based threats Minimize on-premises Active Directory and migrate to Microsoft Entra ID Who this book is for This book is for identity administrators, system administrators, security engineers, cloud architects, and IT professionals responsible for securing and governing enterprise access. If you manage hybrid environments, enforce Zero Trust, automate lifecycle processes, or protect against identity-based threats, this guide is for you. A foundational understanding of networking, IAM, and cloud concepts will help you get the most from this book.

Master Docker, Kubernetes, and cloud-native container deployment with this hands-on guide. Learn image creation, orchestration, security, monitoring, and real-world production patterns for scalable applications Key Features Learn Docker and Kubernetes from first principles through hands-on, production-focused examples Build secure, scalable container platforms with orchestration, governance, and cloud best practices Apply modern DevOps and AI-driven automation patterns in real-world enterprise scenarios Book DescriptionContainers have become the foundation of modern software platforms, transforming how applications are built, shipped, secured, and operated. However, as systems grow more distributed and regulated, using containers effectively requires more than basic commands; it requires architectural understanding, security awareness, and operational discipline. The Ultimate Docker Container Book, Fourth Edition, takes you from container fundamentals to running production-grade platforms. Starting from first principles, the book explains how containers reduce friction in the software supply chain and progressively introduces images, networking, state management, testing, and debugging. You will learn how to design and operate distributed applications, manage multi-service systems, and apply orchestration using Kubernetes. This fourth edition places a stronger emphasis on security, governance, and compliance, reflecting real-world enterprise requirements. It also explores AI and automation in DevOps, showing how modern teams can enhance delivery and operations responsibly. Whether you are a developer, DevOps engineer, platform engineer, or software architect, this book equips you with the skills and understanding needed to build secure, scalable, and future-ready container platforms.What you will learn Understand containers and their role in the modern software supply chain Build, run, debug, and test containerized applications effectively Create optimized and secure container images Manage state, configuration, and networking for containers Design and operate distributed applications using orchestration Deploy and secure applications with Kubernetes in production Run container platforms on major cloud providers Apply security, governance, and AI-driven automation best practices Who this book is for This book is for Linux professionals, system administrators, DevOps engineers, operations engineers, software architects, and developers looking to master Docker, Kubernetes, and cloud-native containerization. A basic understanding of Docker is useful, but no prior Kubernetes experience is required. Familiarity with Bash or PowerShell helps but is not mandatory.

A practical guide to applying LLMs across the software development and delivery lifecycle, improve development, testing, operations, and project efficiency across modern software organizations. Key Features Apply LLMs to modern DevOps workflows across development and operations with practical enterprise examples Build architectural fluency in GPT, fine-tuning, RAG, and agent-based systems Strengthen software delivery pipelines with AI-informed automation and operational intelligence Book DescriptionIf you work in DevOps, SRE, platform engineering, software delivery, operations, testing, or security, this book shows how large language models (LLMs) can reduce delivery friction, improve operational visibility, and support more reliable engineering workflows. Written by enterprise digital transformation and delivery specialists, it focuses on moving LLMs beyond isolated experiments into practical software delivery systems. You will build the LLM foundations needed to understand modern AI systems, including language model evolution, Transformer architecture, GPT-style generation, and efficient fine-tuning techniques such as LoRA and QLoRA. The book then connects these foundations to enterprise-ready patterns such as retrieval-augmented generation (RAG), multi-agent systems, and platform-based AI assistance. Through operations, testing, coding, project management, and cybersecurity scenarios, you will see how LLMs can support log analysis, ticket handling, root cause analysis, test generation, code generation, risk management, and security workflows. By the end of the book, you will understand how to move from model experimentation to practical AI-assisted delivery, evaluate where LLMs create measurable value across DevOps, SRE, and platform engineering workflows, and recognize the constraints, risks, and governance considerations involved.What you will learn Apply RAG and multi-agent patterns to enterprise software delivery and platform engineering scenarios Use LLMs to support operations tasks such as log analysis, ticket handling, incident response, and root cause analysis Explore how LLMs can improve software testing, static analysis, vulnerability repair, and test automation workflows Apply code LLMs to development workflows, including code generation, completion, review support, and project-level coding tasks Use LLMs to support project management, delivery coordination, risk analysis, and cybersecurity workflows Evaluate the practical value, risks, and constraints of introducing LLMs into DevOps, SRE, and platform engineering environments Who this book is for This book is for software engineers, DevOps and SRE professionals, QA and security teams, and technical managers who want to apply and operationalize LLMs across the software delivery lifecycle.

Learn how to integrate AI into Linux environments with real-world automation, observability, and scalable deployment techniques for modern infrastructure teams Key Features Apply AI to Linux, from core concepts to production-ready deployments at scale Build intelligent automation using LLMs, RAG, and AI agents for monitoring, troubleshooting, and system administration Deploy secure, scalable AI workloads with Docker, Kubernetes, and cloud-native best practices Book DescriptionUnlock the power of artificial intelligence to transform Linux infrastructure and operations. The Ultimate AI Guide for Linux Engineers is a practical, hands-on handbook for applying AI to real-world Linux systems. You will demystify AI, machine learning, and large language models (LLMs) in practice, prepare AI-ready Linux environments for CPU and GPU workloads, and work with containers and essential open-source frameworks such as PyTorch, Hugging Face Transformers, LangChain, and OpenVINO. Moving into real operational use cases, you will build AI agents and agentic workflows to automate system administration, integrate LLMs into monitoring and troubleshooting pipelines, and apply Retrieval-Augmented Generation (RAG) to query logs, documentation, and internal knowledge bases. You will also enhance observability and incident response with intelligent automation. Finally, you will learn how to deploy and scale AI services using Docker, Kubernetes, and cloud-native architectures, implement security and privacy guardrails, and design reliable AI-driven workflows for enterprise Linux environments. By the end, you will have a practical framework to integrate AI into Linux workflows securely and at scale.What you will learn Optimize Linux kernels and GPUs for AI workloads Orchestrate LLM pipelines across distributed systems Design agentic workflows for autonomous operations Implement RAG over logs and internal knowledge graphs Embed AI into observability and incident triage Deploy scalable AI microservices on Kubernetes Enforce security, isolation, and model guardrails Who this book is for This book is for Linux engineers, system administrators, DevOps professionals, SREs, and platform engineers who want to integrate AI into real-world infrastructure and operations. Prior hands-on experience with Linux, the command line, and basic system administration is expected. Some familiarity with containers (Docker), Kubernetes, and scripting (Bash or Python) would be helpful. Prior AI or machine learning knowledge is beneficial but not required, as core concepts are explained in practical Linux terms.

Gain a solid practical understanding and sufficient theoretical insight into Linux kernel internals while learning to write high-quality kernel module code and understanding the complexities of kernel synchronization Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features Discover how to write Linux kernel and module code for real-world products on the 6.1 LTS kernel Implement industry-grade techniques in real-world scenarios for fast, efficient memory allocation and data synchronization Understand and exploit kernel architecture, CPU scheduling, and kernel synchronization techniques Book DescriptionThe 2nd Edition of Linux Kernel Programming is an updated, comprehensive guide for those new to Linux kernel development. Built around the latest 6.1 Long-Term Support (LTS) Linux kernel, which is maintained until December 2026, this edition explores its key features and enhancements. Additionally, with the Civil Infrastructure Project extending support for the 6.1 Super LTS (SLTS) kernel until August 2033, this book will remain relevant for years to come. You'll begin this exciting journey by learning how to build the kernel from source. Step by step, you will then learn how to write your first kernel module by leveraging the kernel's powerful Loadable Kernel Module (LKM) framework. With this foundation, you will delve into key kernel internals topics including Linux kernel architecture, memory management, and CPU (task) scheduling. You'll finish with understanding the deep issues of concurrency, and gain insight into how they can be addressed with various synchronization/locking technologies (for example, mutexes, spinlocks, atomic/refcount operators, rw-spinlocks and even lock-free technologies such as per-CPU and RCU). By the end of this book, you'll build a strong understanding of the fundamentals to writing the Linux kernel and kernel module code that can straight away be used in real-world projects and products.What you will learn Configure and build the 6.1 LTS kernel from source Write high-quality modular kernel code (LKM framework) for 6.x kernels Explore modern Linux kernel architecture Get to grips with key internals details regarding memory management within the kernel Understand and work with various dynamic kernel memory alloc/dealloc APIs Discover key internals aspects regarding CPU scheduling within the kernel, including cgroups v2 Gain a deeper understanding of kernel concurrency issues Learn how to work with key kernel synchronization primitives Who this book is for This book is for beginner Linux programmers and developers looking to get started with the Linux kernel, providing a knowledge base to understand required kernel internal topics and overcome frequent and common development issues. A basic understanding of Linux CLI and C programming is assumed.

The official SUSE Linux guide to mastering Linux administration. Deploy, secure, and manage SLES 16 servers while preparing for the SCA certification Key Features Build real-world SLES 16 sysadmin skills through hands-on tasks and examples Secure and optimize enterprise Linux systems using mcphost, cockpit, firewalld, SELinux, and system roles Prepare for the SUSE Certified Administrator (SCA) exam with guided exercises Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionMastering enterprise Linux administration can be overwhelming without guidance, especially for those targeting the SUSE Certified Administrator (SCA) certification or managing critical IT infrastructures. This practical guide offers the complete skillset required to confidently operate, secure, and maintain SUSE Linux Enterprise Server (SLES) 16. Written by senior SUSE product experts, this book helps you deploy physical and cloud systems, configure software and networking, secure services using firewalld and SELinux, and manage storage using BTRFS and LVM. With step-by-step instructions, real-world examples, and detailed chapters on automation with system roles and containers with Podman, you’ll build confidence across core Linux administration topics. You’ll also gain hands-on practice with system snapshots, kernel tuning, and AI with mcphost. It also includes a brief introduction to SLES 4 SAP-specific features. You'll learn how to configure services, harden systems, automate infrastructure with tools like Agama, Cockpit, and System Roles. This is the only official guide for SLE administrators and engineers, providing trusted insight you will not find anywhere else. By the end, you will be well-equipped to manage SLES systems in enterprise environments and approach the SCA certification with confidence. What you will learn Install and configure SLES 16 on physical and cloud platforms Manage users, permissions, and network settings Secure systems using firewalld and SELinux Administer remote access with SSH, Cockpit, and tunnels Use Agentic AI with mcphost Handle storage with BTRFS, LVM, and NFS Tune system performance and kernel parameters Use automation tools like Ansible and system roles Deploy and manage containers using Podman Who this book is for This book is for system administrators, platform engineers, DevOps professionals, developers, and certification aspirants looking to strengthen their Linux expertise, prepare for the SUSE Certified Administrator (SCA) exam, and gain practical skills for managing IT infrastructures. It is especially valuable for those working with SAP environments on Linux, where reliability and performance are critical. Prior Linux knowledge isn’t required, but familiarity with basic IT concepts will be helpful.

Improve your Azure architecture practice and set out on a cloud and cloud-native journey with this Azure cloud native architecture guide Key FeaturesDiscover the key drivers of successful Azure architectureImplement architecture maps as a compass to tackle any challengeUnderstand architecture maps in detail with the help of practical use casesBook Description Azure offers a wide range of services that enable a million ways to architect your solutions. Complete with original maps and expert analysis, this book will help you to explore Azure and choose the best solutions for your unique requirements. Starting with the key aspects of architecture, this book shows you how to map different architectural perspectives and covers a variety of use cases for each architectural discipline. You'll get acquainted with the basic cloud vocabulary and learn which strategic aspects to consider for a successful cloud journey. As you advance through the chapters, you'll understand technical considerations from the perspective of a solutions architect. You'll then explore infrastructure aspects, such as network, disaster recovery, and high availability, and leverage Infrastructure as Code (IaC) through ARM templates, Bicep, and Terraform. The book also guides you through cloud design patterns, distributed architecture, and ecosystem solutions, such as Dapr, from an application architect's perspective. You'll work with both traditional (ETL and OLAP) and modern data practices (big data and advanced analytics) in the cloud and finally get to grips with cloud native security. By the end of this book, you'll have picked up best practices and more rounded knowledge of the different architectural perspectives. What you will learnGain overarching architectural knowledge of the Microsoft Azure cloud platformExplore the possibilities of building a full Azure solution by considering different architectural perspectivesImplement best practices for architecting and deploying Azure infrastructureReview different patterns for building a distributed application with ecosystem frameworks and solutionsGet to grips with cloud-native concepts using containerized workloadsWork with AKS (Azure Kubernetes Service) and use it with service mesh technologies to design a microservices hosting platformWho this book is for This book is for aspiring Azure Architects or anyone who specializes in security, infrastructure, data, and application architecture. If you are a developer or infrastructure engineer looking to enhance your Azure knowledge, you'll find this book useful.

Build, deploy, and monitor AI applications using Microsoft Foundry's unified portal, combining models, workflows, agents, and responsible AI practices into a single development experience. Key Features Build, evaluate, and deploy AI solutions using Microsoft Foundry’s unified portal Implement guardrails, evaluations, and monitoring for production-ready AI systems Includes real-world use cases integrating Azure OpenAI and Databricks Purchase of the print or Kindle book includes a free PDF eBook Book Description Unlock the full potential of AI with Microsoft Foundry, Microsoft’s unified platform for building, orchestrating, and operating AI solutions at scale. This hands-on guide walks you through the full AI application lifecycle, from data preparation and model selection to deployment, monitoring, and continuous evaluation. Written by Eduardo Sojo, a former Microsoft consultant and current solutions architect at Databricks with over two decades of experience, the book focuses on practical implementation using the latest Microsoft Foundry portal. You’ll learn to design intelligent workflows, build agents, and apply guardrails to ensure safe, reliable AI behavior in real-world scenarios. Rather than focusing only on models, the book shows how to connect workflows, agents, evaluations, and observability so solutions are not just functional, but production-ready. You’ll explore real-world use cases such as building copilots, integrating external systems like Databricks, and creating multi-agent architectures that work with enterprise data. By the end, you’ll be able to design, build, and operate secure, scalable, intelligent AI solutions using Microsoft Foundry. What you will learn Select and deploy models for vision, language, and custom AI scenarios Use Prompt Flow to design and refine complex AI interactions Evaluate models using standard performance metrics Build assistants, copilots, and image-processing solutions with Azure OpenAI Design, deploy, and orchestrate multi-agent systems using Microsoft Foundry Agent Service Utilize Microsoft Foundry Observability for real-time monitoring and evaluation Apply Microsoft's Responsible AI principles to real applications Who this book is for This book is for developers, data professionals, and AI practitioners who want to build and deploy intelligent applications using Microsoft Foundry. Whether you're transitioning from Azure AI Studio or starting fresh, this guide will help you understand how to design real-world AI systems using modern tools and best practices. Basic knowledge of programming and data concepts will help you get the most out of this book.

Prepare for the AWS Certified Cloud Practitioner CLF-C02 exam with this practical AWS study guide covering cloud fundamentals, core AWS services, security, pricing, governance, and real-world cloud scenarios. Key Features Learn AWS through one company's real business problems, not isolated service descriptions Fully updated for the current CLF-C02 exam blueprint, including the revised AWS Free Plan credits model Exam Readiness Drills plus linked online quizzes after every chapter Get a free PDF eBook with print or Kindle purchase Book DescriptionMost AWS certification guides teach services in isolation, leaving you to figure out how they fit together. This guide takes a different approach. You'll work through the real business needs of Belly Brew Co. Limited, a fictional company, using its challenges to learn why you'd choose one AWS service over another, not just what each one does. Fully updated for the current CLF-C02 exam blueprint, this guide covers the revised AWS Free Plan credits model, current Lambda runtimes, and recent service renames, details many competing guides still get wrong. You'll start with cloud fundamentals, virtualization, and AWS global infrastructure, then secure your environment using IAM, IAM Identity Center, and Amazon Cognito. From there, build hands-on experience across storage, networking, compute, and databases, and explore high availability, monitoring, governance, security, and AI/ML services like Rekognition, Polly, and Textract. The book also covers pricing models, cost optimization, the Cloud Adoption Framework, and the Well-Architected Framework, so you design secure, high-performing, resilient, and cost-efficient solutions. Each chapter pairs with Exam Readiness Drills and linked online quizzes, so you test what you've learned as you go. Includes a free PDF eBook with purchase.What you will learn Understand cloud concepts, service models, and deployment models Explore AWS global infrastructure including Regions, Availability Zones, and edge locations Secure AWS accounts using IAM, MFA, IAM Identity Center Work with core services: S3, VPC, EC2, Lambda, RDS, DynamoDB, Route 53, and CloudFront Apply AI/ML services including Rekognition, Polly, Textract, Athena, and Glue Design secure, high-performing, resilient, and cost-efficient solutions using the Well-Architected Framework Who this book is for This book is for beginners, students, IT professionals, developers, administrators, managers, analysts, and project managers who want to build foundational AWS cloud knowledge while preparing for the AWS Certified Cloud Practitioner CLF-C02 exam. Ideal for those new to cloud computing, moving into cloud roles, supporting cloud projects, or planning advanced AWS certifications. No prior AWS experience is required, though basic IT knowledge is helpful.

Learn from Terraform expert Mark Tinderholt and excel in designing and automating your infrastructure and CI/CD pipelines with Terraform across major cloud platforms and paradigms Get With Your Book: PDF Copy, AI Assistant, and Next-Gen Reader Free Key Features Comprehensive guide to building end-to-end solutions with Terraform using VMs, Kubernetes, and Serverless architectures In-depth coverage of integrating Terraform with HashiCorp tools and popular platforms like Packer, Docker, Kubernetes, and Helm Practical insights on streamlining operations with GitHub Actions CI/CD pipelines using the Gitflow workflow Book DescriptionAs cloud technology and automation evolve, managing infrastructure as code, integrating security, and handling microservices complexity have become critical challenges. This book takes a hands-on approach to teaching Terraform, helping you build efficient cloud infrastructure using real-world scenarios and best practices. It begins with an introduction to Terraform's architecture, covering its command-line interface and HashiCorp Configuration Language. You’ll learn best practices, architectural patterns, and how to implement Terraform across virtual machines, Docker/Kubernetes, serverless environments, and cloud platforms like AWS, Azure, and GCP. The book also covers integrating Terraform into CI/CD pipelines with other technologies to automate infrastructure provisioning and management. Additional chapters focus on security, monitoring, troubleshooting, and cost optimization. You'll also gain insights into preparing for the Terraform Associate certification. By the end, you’ll have the skills to build, automate, and manage cloud infrastructure effectively.What you will learn Explore Terraform architecture and configurations in depth Integrate Packer with Terraform for VM-based solutions Containerize apps with Docker and Kubernetes Explore GitOps and CI/CD deployment patterns Transform existing applications into serverless architectures Migrate and modernize legacy apps for the cloud Implement Terraform on AWS, Azure, and GCP Use Terraform with teams of varying size and responsibility Who this book is for This book is for DevOps engineers, cloud engineers, platform engineers, infrastructure engineers, site reliability engineers, developers, and cloud architects who want to utilize Terraform to automate their cloud infrastructures and streamline software delivery. Prior knowledge of cloud architecture, infrastructure, and platforms, as well as Terraform basics, will help you understand the topics present in this book.

Enhance your Kubernetes skills with Istio integration, security best practices, advanced CI/CD techniques, and effective monitoring using Prometheus and Grafana. Gain expertise in multitenancy, secrets management, and global load balancing to optimize deployments, improve security, and streamline operations in enterprise environments. Key Features Practical insights on running Kubernetes in enterprise environments, backed by real-world experience Strategies for securing clusters with runtime security, direct pod mounting, and Vault integration for secrets management A dual-perspective approach that covers Kubernetes administration and development for a complete understanding Book DescriptionKubernetes – An Enterprise Guide, Third Edition, provides a practical and up-to-date resource for navigating modern cloud-native technologies. This edition covers advanced Kubernetes deployments, security best practices, and key strategies for managing enterprise workloads efficiently. The book explores critical topics such as virtual clusters, container security, and secrets management, offering actionable insights for running Kubernetes in production environments. Learn how to transition to microservices with Istio, implement GitOps and CI/CD for streamlined deployments, and enhance security using OPA/Gatekeeper and KubeArmor. Designed for professionals, this guide equips you with the knowledge to integrate Kubernetes with industry-leading tools and optimize business-critical applications. Stay ahead in the evolving cloud landscape with strategies that drive efficiency, security, and scalability.What you will learn Manage secrets securely using Vault and External Secret Operator Create multitenant clusters with vCluster for isolated environments Monitor Kubernetes clusters with Prometheus and visualize metrics using Grafana Aggregate and analyze logs centrally with OpenSearch for deeper insights Build a CI/CD developer platform by integrating GitLab and ArgoCD Deploy applications in an Istio service mesh and enforce security with OPA and GateKeeper Secure container runtimes and prevent attacks using KubeArmor Who this book is for This book is designed for DevOps engineers, developers, and system administrators looking to deepen their knowledge of Kubernetes for enterprise environments. It is ideal for professionals who want to enhance their skills in containerization, automation, and cloud-native deployments. While prior experience with Docker and Kubernetes is helpful, beginners can get up to speed with the included Kubernetes bootcamp, which provides foundational concepts and a refresher for those needing it.

Design and build Internal Developer Platforms (IDPs) with future-oriented design strategies, using the Platform as a Product mindset Key Features Comprehensive guide to designing platforms that create value and drive user adoption Expert insights on shifting to a product-centric mindset for architects and platform teams Best practices for managing platform complexity, reducing technical debt, and ensuring continuous evolution Book DescriptionAs technology evolves, IT talent shortages and system complexity make it essential to have structured guidance for building scalable, user-focused platforms. This book provides platform engineers and architects with practical strategies to develop internal development platforms that enhance software delivery and operations. You’ll learn how to identify end users, understand their needs, and define platform goals with a focus on self-service solutions for cloud-native environments. Using real-world examples, the book demonstrates how to build platforms within and for the cloud, leveraging Kubernetes. It also explores the benefits of a product-centric approach to platform engineering, emphasizing early end-user involvement and flexible design principles that adapt to future requirements. Additionally, the book covers techniques for maintaining a sustainable platform while minimizing technical debt. By the end, you’ll have the knowledge to design, define, and implement platform capabilities that align with your organization’s goals.What you will learn Make informed decisions aligned with your organization's platform needs Identify missing platform capabilities and manage that technical debt effectively Develop critical user journeys to enhance platform functionality Define platform purpose, principles, and key performance indicators Use data-driven insights to guide product decisions Design and implement platform reference and target architectures Who this book is for This book is for platform engineers, architects, and DevOps professionals responsible for designing and managing internal development platforms. It is also useful for decision-makers involved in optimizing software delivery and operations in cloud-native environments. Familiarity with cloud computing, Kubernetes, and CI/CD concepts is helpful but not required, as the book provides practical guidance on platform engineering, self-service solutions, and managing technical debt.