Books

About the Book Running an LLM is one problem. Understanding its memory footprint, execution phases, and serving behavior is another.LLM Inference Engine Architecture explores the engineering behind language-model inference—from prefill and decode to KV-cache management, attention kernels, quantization, and request scheduling.Written for software engineers and ML engineers with a working knowledge of Python and transformer fundamentals, the book connects mathematical explanations with implementation examples and serving-system design considerations.What You Will ExploreInference fundamentals: Trace the request lifecycle and understand the different computational characteristics of prefill and decode.Memory budgeting: Work through model-weight and KV-cache estimates, including the effects of context length, KV-head count, precision, and concurrent sequences.Attention and memory management: Explore PagedAttention, non-contiguous KV allocation, and the relationship between attention computation and memory access.Quantization: Examine weight and KV-cache precision choices, including FP8 and low-bit weight-quantization approaches.Serving architecture: Study continuous batching, request scheduling, multi-GPU execution, and latency and throughput metrics.Kernel development: Explore CUDA and Triton concepts, kernel fusion, and a quantized matrix-multiplication case study.The book includes numerical examples, reference code, and an educational asynchronous serving simulator. These examples are intended to explain mechanisms and trade-offs—not to replace hardware-specific profiling or production validation.Read the sample to review the mathematical level, coding style, and scope before purchasing.

CIS-SAM is an Implementation Specialist exam — it tests whether you can configure SAM Professional, not whether you know ITAM theory. This guide is organised against ServiceNow's published blueprint, domain by domain, and weights its depth the way the exam does. All five domains across 15 chapters, plus a 60-question practice exam with worked answers.

"Your systems are talking. Are you listening?"In a world where downtime costs millions and silent failures hide in plain sight, observability isn’t just a buzzword—it’s your superpower. But with hundreds of tools, frameworks, and metrics to choose from, how do you cut through the noise?Observability Tools: Design & Comparison Guide is your no-BS playbook to: ✅ Design observability pipelines tailored to AI/ML, microservices, and event-driven architectures. ✅ Compare the top 10 tools—from Datadog to Grafana Stack—and pick the perfect fit for your budget and scale. ✅ Avoid costly mistakes with red flags, alert fatigue fixes, and cost-saving strategies.

Most AIOps material is written by people selling AIOps. This one separates what works in production today from what is still a roadmap promise — and says which is which, chapter by chapter.

Rapid-development frameworks across Ruby on Rails, Node.js, PHP, Python, and .NET revolutionized how quickly we can ship software. But what happens when your business domain grows complex? Relying on Active Record and heavy ORM patterns tightly couples your database to your business logic. As your product matures, this coupling creates brittle, difficult-to-maintain codebases that resist change and slow down your team. The solution is the Domain Model Pattern (the foundation of Domain-Driven Design). It isolates business rules from infrastructure, creating a pure, scalable architecture. Historically, however, this pattern required a massive amount of effort and boilerplate, making it too slow for fast-paced development. That changes today. We are at an AI inflection point. Modern AI coding assistants excel at generating boilerplate and wiring infrastructure, effectively eliminating the traditional drawbacks of DDD. AI finally bridges the gap between the rapid speed of framework scaffolding and the architectural purity of a Domain Model. But to harness AI reliably, you cannot simply let it generate code unguided. You need a structured methodology to guide the machine. In this book, you will learn:How to build reliable, maintainable software using a strict, AI-assisted pipeline: Use Cases → Gherkin → Event Storming → Strategic & Tactical DDD.How to create implementation-agnostic class diagrams that serve as universal blueprints, allowing you to generate code in OOP languages (Java, C#, PHP, Python) or Functional languages (Elixir, Clojure, F#).How to future-proof your systems with a timeless methodology that prioritizes the longevity of business logic over the ephemeral nature of web frameworks.How to scale your architecture gracefully from a Majestic Monolith to Microservices without a complete rewrite. The methodology is language agnostics but in this edition all codes samples are in TypeScript. Whether you want to accelerate your development velocity, improve your system's resilience, or simply reclaim your craft by building beautiful, majestic systems, this book provides the blueprint.Discover how to combine the timeless principles of DDD with the power of AI.

Take your IPv6 skills beyond the basics. Advanced IPv6 Engineering covers real-world network design, routing, security, automation and operations at scale, with practical examples for Linux, enterprise networks, cloud, containers and Kubernetes. Built for engineers who need to deploy, secure and manage IPv6 with confidence.

Learn Argo Rollouts through hands-on Kubernetes labs. Build canary and blue-green deployments, control traffic with Istio, validate releases with Prometheus, run Experiments, automate promotion and rollback, and manage progressive delivery with Argo CD and GitOps.

Linux System Hardening is a practical guide to securing Linux systems from the kernel to the cloud. It explains how threats work, how to apply effective controls and what you give up along the way. Built for admins, security engineers and DevSecOps teams working with real production systems.

Unlock the Power of Google Cloud Platform!"As a professional, I'm not paid just for what I know but for my thought process and the decisions I take to make my architecture optimal." — Sudhanshu Jaiswal🌟 Ever wondered how to harness the full potential of Google Cloud Platform?GCP - A Walkthrough is your ultimate guide to mastering the cloud with expert insights, strategic decision-making, and hands-on best practices. Whether you're migrating workloads, optimizing costs, or building scalable architectures, this book equips you with the knowledge to design, deploy, and manage GCP solutions like a pro.From cost optimization and IAM to big data, AI, and security, dive into a comprehensive walkthrough that transforms your cloud journey. Stop guessing—start optimizing!

Most self-hosted LLM deployments run at 10–30% utilization — full retail price for GPUs that mostly sit idle. Tokens per Dollar is a deliberately short field guide for platform engineers: tune vLLM (2–4x throughput on the same hardware), quantize to halve your GPU count, cache the prompt tokens you keep re-paying for, and autoscale on real token traffic instead of CPU. Working YAML throughout, and a case study that cuts a 70B deployment from $29,200 to $8,600 a month with better latency. Read it in an evening; it pays for itself the first day you apply it.

Infrastructure gets complicated fast. Mastering Terragrunt shows you how to bring order to growing Terraform and OpenTofu environments without adding unnecessary complexity. Learn practical patterns for managing multiple accounts, regions and teams, streamline deployments and build infrastructure that stays maintainable as your organization scales.

Build the Next Generation of Enterprise AI SystemsCloud-Powered Agentic AI is a practical guide to designing, developing, deploying, and operating production-ready AI applications using Large Language Models (LLMs) and modern cloud-native technologies.From prompt engineering and Retrieval-Augmented Generation (RAG) to AI agents, multi-agent systems, Model Context Protocol (MCP), FastAPI, Docker, Kubernetes, and LLMOps, this book provides a complete roadmap for building scalable, secure, and maintainable AI solutions.Through hands-on examples, architecture diagrams, case studies, and a comprehensive capstone project, you will learn how to move beyond prototypes and engineer enterprise-grade AI systems.Inside this book, you'll learn how to:Build LLM-powered applications from scratchDesign Retrieval-Augmented Generation (RAG) pipelinesDevelop AI agents and multi-agent workflowsIntegrate tools using the Model Context Protocol (MCP)Deploy AI services with FastAPI, Docker, and KubernetesApply LLMOps for monitoring, evaluation, and continuous deliverySecure and govern AI applications in enterprise environmentsBuild a complete cloud-native AI platform from development to productionWhether you are an AI engineer, software developer, cloud architect, or machine learning practitioner, this book will equip you with the practical skills needed to build reliable and scalable AI systems in the era of generative AI.

Cloud networks rarely fail in obvious ways. A DNS lookup breaks, a route disappears or traffic vanishes somewhere between clouds. This book shows you how to understand what is really happening, build networks that hold up under pressure and troubleshoot failures with confidence across AWS, Azure, Google Cloud, Kubernetes and hybrid environments.

Build OpenStack with confidence, from your first deployment to production at scale. This hands-on guide walks you through planning, configuration, security, daily operations and troubleshooting with practical examples and proven techniques. A dependable resource for admins, DevOps engineers and cloud architects.

Cloud migration is more than a technical upgrade—it’s a strategic imperative for modern businesses. In "Cloud Migration: A Journey," you’ll discover how to navigate the complexities of moving workloads to the cloud, whether from on-premises, between cloud providers, or through hybrid approaches. This book equips you with the knowledge, strategies, and best practices