DevOps

> 3:07 a.m. The pager goes off. Your database is down, the disk is full, and you're the on-call engineer holding the line. Open this manual to the playbook you need right now to diagnose, stabilize, and verify the fix -- fast.

Learn how to stop retry storms, contain dependency failures, and make resilient microservice calls observable, bounded, and production-ready.

There's a reason your last incident made it past review. It's not that nobody tested — it's that nobody asked the right question. This book is 745 of the right questions, sorted by what happens if you skip them.

Build your own 24/7 internet radio in a single weekend — on a €5 server, understood down to the last line. Ten chapters from bare server to on-air: the tower (Icecast), the robot (Liquidsoap), the door (nginx). Every step tested live, every trap marked. Friday it's a napkin sketch; Sunday night a stranger presses play, and it plays.

Your DevOps pipeline is broken. PRs rot in review. Alerts scream into the void. Incidents drag on for hours. And capacity planning? A guessing game with your budget on the line.AI in DevOps isn’t about hype—it’s about fixing what’s actually broken. This book gives you: LLM-powered code reviews Intelligent alerting Auto-remediation Predictive scaling

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.

Tired of 2 AM fire drills because a rogue container slipped into production? Policy-as-Code Field Guide is your battle-tested playbook for automating compliance, security, and operational guardrails—before they become incidents.From OPA’s Rego to Kyverno’s YAML, Conftest’s pre-commit checks to Sentinel’s Terraform controls, this guide cuts through the noise with real-world policies, CI/CD integrations, and production hardening tips you can use today.Shift left. Enforce early. Sleep better.

Stop Guessing. Start Dominating Your DevOps Interview.You’ve spent years mastering GCP, Terraform, Kubernetes, and CI/CD—but can you articulate your expertise under pressure? Most candidates stumble on strategic framing, production scenarios, or leadership questions. This guide changes that.What’s Inside? 🔹 End-to-End CI/CD Pipeline Design – From GitHub Actions to ArgoCD, with security gates and rollback strategies. 🔹 Secure Multi-Env GCP Infrastructure – Private GKE clusters, Workload Identity, Secret Manager, and VPC Service Controls—all production-ready. 🔹 Kubernetes + ArgoCD at Scale – App-of-Apps patterns, RBAC, and zero-downtime deployments for multi-team platforms. 🔹 Python for DevOps Automation – Audit IAM bindings, build operators, and automate workflows like a pro. 🔹 Leadership & Behavioral Mastery – STAR method stories for incidents, tool adoption, and mentorship.Why This Guide? ✔ No Fluff – Only actionable, interview-tested content. ✔ Real-World Code – Terraform modules, ArgoCD manifests, and Python scripts you can use today. ✔ Interviewer Mindset – Learn what hiring managers actually look for in a Lead DevOps Engineer.Your Next Career Move Starts Here. 📖 Grab your copy and walk into your interview prepared to impress.

A practical guide to operating a fleet of AI coding agents through routing, memory, skills, MCP, guardrails, and a persistent control plane (286 manuscript pages).

Kubernetes exposes plenty of state, but operators still have to reconstruct operational meaning from scattered Pods, Services, EndpointSlices, Events, PVCs, owner references, and status fields. This ebook uses `kctx`, a small read-only Kubernetes context engine, to show how deterministic entities, relations, signals, graphs, namespace snapshots, CRD adapters, and stable JSON contracts can turn raw cluster data into reusable context for humans, tools, and AI agents. It is written for SREs, platform engineers, Kubernetes operators, infrastructure developers, and AI tooling builders who want better primitives than raw YAML and improvised troubleshooting pipelines.

Your apps are failing. You just don’t know it yet.What if you could see every crash, slowdown, and bottleneck before your users do? Understanding Dynatrace pulls back the curtain on AI-powered observability—turning blind spots into actionable insights. Stop guessing. Start observing.

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.

大多数 Kubernetes 书籍教你 YAML。这本书教你构建一个数据中心。 你从 #!/usr/bin/env bash 开始。最终用一个命令就能配置、重载和拆除一个完整的多服务集群。没有魔法。没有黑盒。只有你、Bash，以及你亲手构建的云原生平台。

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.

A leader at a company opens Slack one morning. The brief their virtual employee wrote looks normal: same headers, same prose voice, same cadence as every other brief that week. The last line ends mid-sentence. The API returned `stop_reason: "max_tokens"`. The system shipped it anyway. No exception. No log line. No retry. That bug doesn't look like the normal kind. This book is about why, and what to build instead.