Cloud Computing

Cloud Computing: From Fundamentals to Advanced Concepts is a comprehensive guide that takes you from the basics of cloud computing to advanced topics including virtualization, cloud architecture, cloud security, cloud storage, Service-Oriented Architecture (SOA), cloud governance, benchmarking, analytics, and real-world cloud implementations.Written for BCA, B.Tech, MCA students.

"Your Kubernetes cluster is one expired certificate away from disaster. Are you ready?" Master Certificate Management in Kubernetes—automate, secure, and scale TLS like a pro. No more downtime, no more manual renewals. Just peace of mind.

WebAssembly is becoming the standard for portable, secure and high-performance software across browsers, the cloud, edge computing and embedded systems. This book explains how Wasm works, why it matters and how to build with it confidently.

A model you rent can be repriced, throttled or revoked. A model you own cannot. Build a sovereign local AI factory on commodity enterprise hardware: Apache CloudStack, frontier-class open models in 768GB of RAM, GPU passthrough, RAG, fine-tuning, and a multi-agent support desk — with nothing leaving the building. You don't need to be a hyperscaler. Grow your own, cook your own.

80 real-world troubleshooting scenarios. 8 critical technologies. 1 book to end your on-call nightmares.From Ansible’s Jinja2 errors to Kubernetes cascading failures, Production Grade Troubleshooting gives you step-by-step fixes for the issues that break production—before they break you.Debug like a pro. Sleep like a boss.

AWS Amplify Gen 2 lets you build full-stack applications on AWS with a TypeScript-first workflow. This book covers authentication, data modeling, serverless backends, AI integrations, security and deployment through practical examples that help you build production-ready applications faster.

Quantum computing is ready for developers. Quantum Applications on AWS Braket is a practical, code-first guide to building and running quantum applications on AWS Braket with hands-on Python examples and real quantum hardware.

Oracle Cloud Infrastructure is powering more enterprise workloads than ever, and this practical guide helps you get the most from it. Covering core infrastructure, networking, security, automation, AI, analytics and disaster recovery, it gives architects, administrators, developers and DevOps professionals the skills to design, deploy and manage solutions on OCI.

Google Cloud Platform Essentials is a practical guide to Google Cloud, covering compute, storage, networking, databases, Kubernetes, serverless, security, AI/ML and more. With hands-on examples and practical best practices, you'll learn to build scalable, reliable and cost-effective cloud applications while preparing for GCP certifications.

Azure from Zero to Production is a practical guide for engineers who want to build secure, reliable and automated platforms on Microsoft Azure. It covers real-world architecture, infrastructure automation, deployments, cost and performance optimization and day-to-day operations, helping you take workloads from development to production with confidence.

Whether you're deploying your first AWS application or managing cloud infrastructure at scale, The Practical AWS Guide helps you design, build, secure, automate and operate reliable AWS environments. Through practical examples, real-world architectures and proven best practices, you'll learn the skills needed to build scalable, secure and cost-effective solutions with confidence.

What if learning Kubernetes felt like reading a story instead of a manual? Follow three engineers as they grow from a crashing monolith to a production-grade Kubernetes platform. Every concept taught through real problems, real YAML, and real solutions.

An enterprise-focused guide to building a safe, governed AI SRE agent skill that reviews Kubernetes platforms on Talos Linux. Read-only by default, auditable, and grounded in evidence rather than guesswork.

Go from an empty cluster to a self-deploying GitOps pipeline, doing every step yourself: Applications, sync policy, hooks, RBAC, Helm, ApplicationSets, and a setup that ships every branch on push. You finish with a pipeline you can explain line by line, not a demo you watched run.

Your first AI integration is usually simple: one provider, one SDK, one model. Then production happens. Costs rise. Rate limits appear. Teams need access control. Security wants API keys centralized. Product wants to test better models. Finance wants to know who is spending what. Suddenly, the real problem is not calling an LLM. The real problem is operating AI as shared infrastructure. The End of AI Vendor Lock-In shows how LiteLLM helps teams build provider-agnostic, resilient, cost-aware AI systems that can route across models, manage fallbacks, track spend, enforce budgets, and reduce dependency on any single provider. If you are building AI products that need to survive real-world production pressure, this book is for you.