Cloud Computing

Explore how a Modern Application Strategy book can take your organization from the basics to the cutting edge of innovation. Learn practical ways to modernize old systems with real-world examples from containers, serverless computing, and microservices. Your guide to technological evolution is here.

Digitale Transformation war das Hype-Thema vor KI. Es gibt allerdings einen nicht geringen Anteil an Fach- und Führungskräften, deren Wissen darüber weiter ausbaufähig ist. Dieses Buch ist eine Einführung für alle diejenigen, die keine "digitalen Pioniere" sind, sondern wissen wollen, was aus dem Thema Digitalisierung auf sie zukommen kann und aus dem Thema KI zusätzlich auf sie zukommen wird.

Compilation of best practices for evaluating weak spots in your product and company and fostering a robust security culture within your organization.

In diesem Primer erklären wir Data Mesh aus der Engineering-Perspektive.

Master Kubernetes v1.36+ on your laptop without cloud bills. Build production-grade multi-node clusters using Kind (Kubernetes in Docker). From architecture basics to Ingress, Gateway API, and RBAC.

Domina Kubernetes v1.36+ en tu portátil sin costes de nube. Despliega clusters multi-nodo profesionales usando Kind. Desde arquitectura básica hasta conceptos avanzados como Gateway API, Ingress y RBAC.

Программирование всегда доступных, распределенных, обслуживающих миллионы запросов систем основано на возможностях облаков Cloud. Книга расскажет, как создавать приложения согласно концепции Cloud Native, "рожденные" жить и работать в облаке - а именно видеть их как набор микросервисов, упаковывать в образы Docker, и управлять ими в Kubernetes.

Understand DynamoDB through visual explanations of access patterns, keys, queries, consistency, data modeling, and reliable operations.

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.

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.