Cloud Computing

Explore the art and science of privilege escalation across modern operating systems, enterprise networks and cloud environments. From system internals and misconfigurations to advanced exploitation techniques, this book delivers the technical depth, practical insights and defensive knowledge needed to uncover privilege escalation paths and strengthen security in complex infrastructures.

This book is for Go programmers who know a little networking, and for network engineers who know a little Go. There's no need to be an expert in either. So, in these chapters, we're building a tool together, Netkit, and it grows a bit more every time we solve a problem. And each chapter kicks off with a situation you might recognise, like a supplier recall, a partner network that's all over the place, or a backup that drags on for no good reason.

🚀 New book: Understanding Kyverno in a visual way! Want to master Kubernetes policies without getting lost in YAML, CEL expressions, and complex concepts? Discover Kyverno through clear, colorful sketchnotes! From policy basics to advanced policy management, security, testing, and troubleshooting, this book makes Kyverno easier to understand, one visual at a time. 📘 Updated for Kyverno 1.19 (August 2026)Learn visually. Understand better. Use Kyverno wisely!

Master practical system design with 62 parts covering distributed systems, scalability, databases, security, and production architecture. A comprehensive engineering handbook with real-world examples, diagrams, and decision frameworks.

Stop relying on abstract marketing buzzwords. Master the real technical architecture, decision trees, and production-grade ABAP Cloud patterns needed to build upgrade-stable Clean Core extensions on SAP S/4HANA and SAP BTP.

Ready to take your infrastructure skills to the next level? Mastering Pulumi shows you how to build, deploy and manage real-world infrastructure across multiple clouds using familiar programming languages. From your first project to complex enterprise platforms, discover practical techniques and proven patterns to make infrastructure more reliable, secure and easier to maintain.

Understand DynamoDB from data modeling and access patterns through security, production operations, recovery, and multi-region delivery.

Learn Packer through visual explanations of repeatable machine images, templates, builders, provisioning, and image delivery.

Understand how Azure services fit together, then design, implement, and troubleshoot practical cloud systems. Visual explanations connect identity, governance, networking, applications, automation, observability, resilience, and day-2 operations.

Your AI application works on your machine. What happens when it reaches production? Learn to deploy, monitor, secure, and scale AI systems using Docker, CI/CD, Kubernetes, cloud infrastructure, and practical production engineering. Move beyond prototypes and build AI applications you can operate with confidence.

This book is not just a Python course with a networking flavour or a networking course with a few scripts attached. You already know how routing converges, why VLANs leak, and what a half-open TCP session looks like on the wire. What this book adds is the ability to transform that knowledge into code that runs across hundreds of devices, reports accurately on its findings and never makes changes that cannot be undone.

사내 LLM 게이트웨이를 설계하고 운영하는 실무서입니다. 키리스 인증과 6단계 RBAC, 토큰 쿼터·예산과 비용 통제, 엔터프라이즈 규모 MCP, 운영 런북까지 다룹니다. 영문판 The AI Gateway Playbook의 한국어판(PDF 75쪽)입니다.

A practitioner's playbook for building an in-house LLM gateway: keyless auth, six-tier RBAC, quotas and budgets, per-token cost control, MCP at enterprise scale, and the operations to run it all. About 32,000 words with diagrams, configs, and checklists.

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.