Nolan Veyne (66 results)

Paperback. Condition: new. Paperback. Your system is running. But is it actually reliable?A service can pass every test, deploy successfully, and look healthy on a dashboard-then collapse when traffic spikes, a dependency slows down, Kubernetes reschedules workloads, a bad release reaches production, or retries turn a small failure into a cascading outage.If you work with modern production systems, you already know the challenge. Keeping software running is not the same as engineering reliability. Whether you're exploring SRE for DevOps engineers, platform engineering, cloud operations, or backend development, you need to know what to measure, when to alert, how to diagnose failures, how to contain their blast radius, and how to recover without relying on guesswork.Production Site Reliability Engineering provides a practical, systems-focused approach to site reliability engineering and production reliability engineering, showing you how to design, operate, troubleshoot, and continuously improve dependable production environments. Instead of treating SRE as a collection of disconnected tools, this hands-on guide brings together cloud native reliability, observability engineering, Kubernetes production operations, distributed systems reliability, production incident management, and safe automation as parts of one coherent reliability system.Inside, you'll learn how to: Design meaningful SLIs, SLOs, error budgets, and burn-rate alerts, and apply SLO error budget monitoring to real user journeys and engineering decisions.Build production observability with Prometheus metrics, structured logs, distributed tracing, OpenTelemetry, RED and USE signals, and evidence-driven dashboards.Diagnose and manage production incidents systematically using timelines, telemetry correlation, failure-domain isolation, hypotheses, mitigation, recovery verification, runbooks, and postmortems.Engineer reliable Kubernetes workloads with health probes, resource controls, graceful termination, workload QoS, autoscaling, disruption budgets, stateful workloads, and resilient placement.Prevent cascading distributed-system failures using deadlines, timeouts, retry budgets, exponential backoff, jitter, circuit breakers, bulkheads, load shedding, and graceful degradation.Design reliable asynchronous systems with queues, backpressure, idempotency, dead-letter handling, replay, and controlled recovery.Plan capacity and engineer performance using throughput, concurrency, saturation, tail latency, load testing, forecasting, autoscaling, and production headroom.Validate resilience before disaster strikes through failure injection, chaos engineering, disaster recovery, game days, backup and restore verification, RTO, RPO, and recovery drills.Reduce deployment and operational risk with CI/CD reliability gates, rolling and blue-green deployments, canary releases, progressive delivery, automated remediation, operational guardrails, and AI-assisted SRE.Apply these practices to an evolving Kubernetes-based cloud-native commerce system, connecting reliability requirements to realistic production failures and evidence-driven engineering decisions.This isn't a book about achieving reliability by adding more dashboards, replicas, alerts, or automation. Reliability is demonstrated by how your system behaves when production stops behaving as expected-and by your ability to prove, diagnose, and improve that behavior.Ready to build production systems that survive failure, recover safely, and improve through evidence? Get your copy of Production Site R Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Taschenbuch. Condition: Neu. Neuware.

Taschenbuch. Condition: Neu. Neuware.

Paperback. Condition: new. Paperback. Your system is running. But is it actually reliable?A service can pass every test, deploy successfully, and look healthy on a dashboard-then collapse when traffic spikes, a dependency slows down, Kubernetes reschedules workloads, a bad release reaches production, or retries turn a small failure into a cascading outage.If you work with modern production systems, you already know the challenge. Keeping software running is not the same as engineering reliability. Whether you're exploring SRE for DevOps engineers, platform engineering, cloud operations, or backend development, you need to know what to measure, when to alert, how to diagnose failures, how to contain their blast radius, and how to recover without relying on guesswork.Production Site Reliability Engineering provides a practical, systems-focused approach to site reliability engineering and production reliability engineering, showing you how to design, operate, troubleshoot, and continuously improve dependable production environments. Instead of treating SRE as a collection of disconnected tools, this hands-on guide brings together cloud native reliability, observability engineering, Kubernetes production operations, distributed systems reliability, production incident management, and safe automation as parts of one coherent reliability system.Inside, you'll learn how to: Design meaningful SLIs, SLOs, error budgets, and burn-rate alerts, and apply SLO error budget monitoring to real user journeys and engineering decisions.Build production observability with Prometheus metrics, structured logs, distributed tracing, OpenTelemetry, RED and USE signals, and evidence-driven dashboards.Diagnose and manage production incidents systematically using timelines, telemetry correlation, failure-domain isolation, hypotheses, mitigation, recovery verification, runbooks, and postmortems.Engineer reliable Kubernetes workloads with health probes, resource controls, graceful termination, workload QoS, autoscaling, disruption budgets, stateful workloads, and resilient placement.Prevent cascading distributed-system failures using deadlines, timeouts, retry budgets, exponential backoff, jitter, circuit breakers, bulkheads, load shedding, and graceful degradation.Design reliable asynchronous systems with queues, backpressure, idempotency, dead-letter handling, replay, and controlled recovery.Plan capacity and engineer performance using throughput, concurrency, saturation, tail latency, load testing, forecasting, autoscaling, and production headroom.Validate resilience before disaster strikes through failure injection, chaos engineering, disaster recovery, game days, backup and restore verification, RTO, RPO, and recovery drills.Reduce deployment and operational risk with CI/CD reliability gates, rolling and blue-green deployments, canary releases, progressive delivery, automated remediation, operational guardrails, and AI-assisted SRE.Apply these practices to an evolving Kubernetes-based cloud-native commerce system, connecting reliability requirements to realistic production failures and evidence-driven engineering decisions.This isn't a book about achieving reliability by adding more dashboards, replicas, alerts, or automation. Reliability is demonstrated by how your system behaves when production stops behaving as expected-and by your ability to prove, diagnose, and improve that behavior.Ready to build production systems that survive failure, recover safely, and improve through evidence? Get your copy of Prod Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Taschenbuch. Condition: Neu. Neuware.

Taschenbuch. Condition: Neu. Neuware - Your Zephyr application builds. It runs on the board. But is it actually ready for production Getting firmware to work is only the beginning. Real embedded firmware development gets harder when a device leaves the development bench. Concurrency bugs emerge under load. Hardware revisions break assumptions. Memory and power become constrained. Networks disappear. Updates fail. And deployed devices may need to remain secure and maintainable for years.If you're learning embedded systems programming or working with real time operating systems, it's easy to know individual APIs while still struggling to understand how Kconfig, Devicetree, device drivers, threads, networking, storage, security, and firmware updates fit together.Zephyr RTOS Engineering bridges that gap.Rather than treating Zephyr as a collection of isolated features, this hands-on guide teaches you how to build portable, connected, secure, testable, and production-grade embedded systems. You'll learn not only how Zephyr mechanisms work, but when to use them, what trade-offs they introduce, and how to validate them.Inside, you'll learn how to: - Master Zephyr development with West, CMake, Kconfig, Devicetree, boards, build artifacts, and reproducible workflows.- Design reliable real-time firmware through RTOS concurrency programming with threads, scheduling, interrupts, work queues, synchronization, and message queues.- Integrate real hardware using GPIO, I C, SPI, UART, sensors, Devicetree, device drivers, and custom driver architectures.- Build portable firmware across boards, peripherals, hardware revisions, and product variants.- Engineer embedded IoT development with IP networking, sockets, MQTT, Bluetooth Low Energy, TLS, reconnection, and offline operation.- Strengthen embedded systems security with threat modeling, device identity, credential protection, secure boot, and signed firmware.- Master MCUboot firmware updates with flash partitions, DFU, OTA delivery, validation, rollback, recovery, and version management.- Optimize constrained devices by measuring flash, RAM, stack, CPU, storage, logging, and power consumption.- Build firmware testing and debugging workflows with Ztest, Twister, GDB, watchdogs, HIL testing, fault injection, and root-cause analysis.- Build embedded CI/CD pipelines connecting automated builds, tests, HIL validation, security gates, signing, and controlled releases.This isn't a book about making one demo work. It is about developing the engineering judgment required to build firmware whose behavior can be reasoned about, measured, reproduced, diagnosed, secured, recovered, updated, ported, and maintained throughout the product lifecycle.If you're ready to move beyond Zephyr tutorials and start engineering embedded systems you can confidently take from development board to production, get your copy of Zephyr RTOS Engineering today.…

Taschenbuch. Condition: Neu. Neuware.

Taschenbuch. Condition: Neu. Neuware.

Taschenbuch. Condition: Neu. Neuware.

Taschenbuch. Condition: Neu. Neuware.

Taschenbuch. Condition: Neu. Neuware.

Taschenbuch. Condition: Neu. Neuware.

Taschenbuch. Condition: Neu. Neuware.

Paperback. Condition: new. Paperback. Are you drowning in data but starving for a decision? Every manager knows the feeling of Conference Room B at 6:40 p.m. You have a looming deadline, a room full of people who are not wrong so much as differently right, and a critical decision with your name on it. Nobody hired you to decide when the answer was obvious; you were hired to make the calls where smart, well-intentioned people look at the same facts and arrive at different conclusions. In The Decisive Manager, Nolan J. Veyne delivers a practical, battle-tested blueprint for leaders who must make high-stakes choices under pressure, uncertainty, and incomplete information. Eschewing neat slide-deck theories that fall apart under real pressure, this book focuses on the specific, ordinary moments where management gets uncomfortable-giving you a small, sturdy set of tools you can actually reach for on a Tuesday afternoon.What's Inside the Decisive Manager Operating System: Across 15 chapters, Veyne lays out a repeatable operating system to help you think better, decide at the correct speed, and lead with confidence: Part 1: Define the Decision Before You Solve It. Stop having arguments that sound productive but solve completely different problems. Learn to write a single Decision Sentence, build a Decision Criteria Card to weight and align your team on values before comparing options, and use the Enough-to-Decide Test to catch stalling disguised as thoroughness.Part 2: Think Better, Not Longer. Expose confirmation bias by building an Opposite Case-the argument a smart, well-informed skeptic would make against your preferred choice. Use R.E.A.L. (Relevant, Evidence, Assumption, Limit) to test every piece of incoming data, and run a Same/Different Check to stop pattern-matching too fast based on past experience.Part 3: Decide at the Right Speed. Diagnostic tools like the Decision Speed Matrix show you when to Move Now, Test First, or Analyze Carefully. Stop consensus-seeking meetings that drag on, map clear Decision Owners, and calculate the hidden Cost of Delay.Part 4: Risk, Uncertainty, and Bad Outcomes. You can't eliminate risk, but you can make failure survivable. Structure a Downside & Recovery Plan, use the Fresh-Start Test to escape sunk-cost thinking, and learn the hardest discipline of all: judging decision quality by what was knowable at the time-not merely by how it turned out.Part 5: Decide, Communicate, and Learn. Close decisions cleanly with a Decision Statement. Change your mind and reverse course without losing credibility when the evidence changes, and build a Decision Journal to turn your choices into compounding skills.Meet Your Peers in the Pages. Follow the journeys of four relatable manager composites: Sarah, who over-analyses and over-collects reports to delay comfortable commitment.Daniel, who pattern-matches too quickly to past successes.Priya, who gets stuck waiting for evidence that does not exist yet.Marcus, who rushes irreversible decisions at the same speed as reversible ones.The book closes with an actionable 30-Day Decision Reset-fifteen minutes a day to build these habits gradually into your actual routine. Good management is not the ability to always know the right answer; it is the ability to make a responsible decision before certainty arrives. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condition: new. Paperback. Are you still fixing someone else's slide deck at 6:15 on a Tuesday evening, quietly telling yourself "it's just faster this way"? You didn't become a leader to spend your evenings approving routine expense claims, clearing endless emails, and putting out daily fires. Yet, Monday morning arrives, your inbox is glowing, and back-to-back meetings start immediately. The problem isn't that you lack technology. It's managerial overload-too much information, too many minor decisions landing on your desk, and almost no time left to think clearly. In The High-Leverage Manager, business author Nolan J. Veyne delivers a practical, fluff-free blueprint for the modern leader. This is not a book of breathless hype or generic prompts. It is a system to build a repeatable management system that protects your most valuable assets: your time and your judgment. The Core Shift: AI Participates. The Manager Owns Judgment. AI can draft an email, summarize a document, or generate a meeting agenda. But it cannot know if the output is fair, true, or right for your specific team. This book teaches you how to stop using AI like a glorified search box and start using it as genuine leverage. You will learn to divide your work into what can be automated, accelerated, augmented, and what must stay human.Real-World Scenarios You'll Recognize Navigate common management struggles alongside four relatable, composite leaders: Sarah, who is so busy helping her team that she has quietly become the bottleneck for every single decision.Daniel, who is drowning in more information and meetings than any calendar should hold.Priya, who struggles with stakeholders and writing massive reports to prove work instead of enabling decisions.Marcus, who runs a fast-moving team where every single break or fire lands on his desk.What You Will Master with the P.A.C.E. System: Built around the core pillars of Prepare, Analyze, Communicate, and Execute, you will unlock 15 practical tools: The Decision-First Meeting: Cut useless, recurring meetings and ensure every slot is built around a real decision.The One-Page Decision Brief: Stop writing 18-page reports and start writing compressed briefs that get immediate sign-off.The Decision Challenger: Use AI to attack your assumptions and stress-test plans instead of outsourcing your confirmation bias.The Delegation Contract: Shift from handing out tasks to delegating true ownership.The Feedback Test & B.R.A.V.E. Conversations: Stop confusing kindness with ambiguity. Say what you mean before small issues escalate.The Five Coaching Questions: Transform from an answer provider into a thinking partner.Exclusive Bonus Material Included: The 30-Day Manager Reset: A daily, step-by-step action plan to install high-leverage habits without collapsing under a busy week.The High-Leverage Toolkit: 50 highly targeted prompts organized by actual management problems (not AI features).The Quick Reference Guide: A fifteen-tool cheat sheet for rapid, Tuesday-morning implementation.If you are ready to stop reacting to the loudest email and start spending your time on the coaching, strategizing, and human relationships that actually require your unique judgment-this book is for you. Stop drowning in overload. Build a system that scales. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Condition: New. Print on Demand.

Condition: New. Print on Demand.

PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condition: new. Paperback. Building an AI prototype is easy. Building an AI system you can actually trust in production is not.A model can generate an impressive response in minutes. But what happens when traffic increases, retrieval becomes stale, an API times out, an agent repeats an action, costs spike, or a model change silently reduces quality?If you're building with LLMs, RAG, AI agents, or generative AI, you've probably discovered that getting the model to work is only the beginning. How should LLM system design handle probabilistic outputs? How do you scale inference reliably? How should RAG architecture deliver fresh, grounded knowledge? How do you manage memory and state, design safe agents, and keep everything secure and observable?System Design for the AI Era bridges the gap between AI experimentation and production AI engineering. Instead of treating the model as the architecture, this book shows you how to design the surrounding generative AI architecture that makes probabilistic AI dependable in production.Inside, you'll learn how to: Design production AI systems around quality, latency, reliability, security, scalability, and costBuild model gateways and model inference architecture with routing, fallbacks, retries, circuit breakers, caching, streaming, and async inferenceEngineer production RAG architecture with ingestion, hybrid retrieval, reranking, grounding, provenance, freshness, and GraphRAGManage context, memory, persistent state, token budgets, privacy, and data lifecycleDesign safe AI agent architecture with tools, checkpoints, durable execution, human approval, authorization, and recoveryBuild LLM evaluation systems with golden datasets, regression testing, guardrails, and quality gatesApply queues, backpressure, load shedding, batching, caching, concurrency controls, capacity planning, and failure injectionImplement AI observability with distributed tracing, telemetry, SLO monitoring, and incident diagnosisEngineer scalable AI infrastructure with containers, Kubernetes, GPUs, workload isolation, and autoscalingApply LLMOps across models, prompts, RAG, evaluation, CI/CD, shadow releases, canaries, rollback, and migrationControl AI economics through cost-aware routing, context budgets, caching, batching, and infrastructure utilizationStudy production architectures for enterprise RAG, customer-support agents, and high-throughput multi-model AI platformsRather than giving you disconnected AI demos, the book develops a progressive production AI reference system, showing how a simple model-backed application evolves as real production requirements emerge.Whether you're a software engineer, AI engineer, backend developer, architect, platform engineer, or technical leader, you'll develop the architectural judgment needed to build AI systems for real users, real traffic, real failures, real security boundaries, and real costs.Stop thinking only about prompts and models. Start thinking like a production AI systems engineer.Get System Design for the AI Era and learn to turn powerful AI capabilities into dependable, scalable, observable, secure, and production-ready systems. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condition: new. Paperback. Automation is everywhere. Control is not.Modern teams can build, deploy, scale, monitor, and remediate software faster than ever. But automation alone does not guarantee a healthy production system. An autoscaler can worsen a database bottleneck. A remediation workflow can execute the wrong recovery perfectly. AI agents for DevOps can diagnose an incident correctly-and still be too dangerous to trust with unrestricted production access.If you work with CI/CD, Kubernetes reliability, observability, SLOs, SRE automation, platform engineering, or DevSecOps automation, you face a growing challenge: these systems increasingly make decisions and change production, yet they are often engineered as separate mechanisms with overlapping objectives and authority.DevOps as a Control System provides a practical production systems engineering framework for bringing them together. You will learn to design production as interconnected feedback loops where telemetry reveals behavior, SLOs define acceptable outcomes, policies constrain decisions, automation acts, and verification determines what happens next.Inside, you will learn how to: Model DevOps as a closed-loop control system using feedback, state, stability, delay, gain, hysteresis, damping, and convergence.Engineer actionable observability with metrics, logs, traces, events, RED, USE, Golden Signals, and business telemetry.Use SLIs, SLOs, error budgets, and burn rates to control reliability and deployment risk.Build feedback-controlled CI/CD and progressive delivery with quality gates, canary releases, production verification, and automated rollback.Apply DevSecOps automation and policy as code to software supply chains, runtime security, governance, and least privilege.Engineer platform engineering and Kubernetes reliability through reconciliation, GitOps, autoscaling, scheduling, and capacity control.Design automated incident remediation and self-healing systems with bounded actions, retries, circuit breakers, verification, rollback, and escalation.Apply AIOps engineering to anomaly detection, event correlation, root-cause analysis, forecasting, and predictive reliability.Engineer AI agents for DevOps and SRE with production context, scoped tools, machine identity, runtime authorization, evaluation, and human oversight.Design autonomous production systems that coordinate multiple controllers while controlling uncertainty, blast radius, authority, and human intervention.A single evolving cloud-native commerce platform connects these concepts, showing how a conventional CI/CD and Kubernetes environment can mature into an observable, secure, resilient, self-healing, intelligent, and governed production system.Whether you are a DevOps engineer, SRE, DevSecOps engineer, platform engineer, cloud engineer, software engineer, AI engineer, or architect, this book will help you move beyond: "How do I automate this?"to the more important question: "How do I build a production system that knows when to act, whether its action worked, and when control must return to a human?"Move beyond automated pipelines. Get DevOps as a Control System and start engineering production systems built for secure, resilient, and governed autonomy. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condition: new. Paperback. The future of production AI isn't one enormous model doing everything. It's the right models, data, retrieval, evaluation, and infrastructure working together for the right tasks.Building a demo with a general-purpose LLM is easier than ever. Building a domain specific AI system that delivers accuracy, predictable latency, controlled cost, privacy, deployment flexibility, and long-term reliability is a very different challenge.If you're an AI engineer, machine learning practitioner, software engineer, architect, or developer moving beyond basic LLM experimentation, you need more than prompts and isolated training recipes. You need a production AI engineering methodology for deciding when small language models are sufficient, when larger models are necessary, what belongs in retrieval, what belongs in model weights, and how to prove that your system is ready for production.Domain-Specific AI Engineering gives you that complete framework: Define Select Curate Adapt Evaluate Optimize Deploy Observe ImproveRather than treating LLM fine tuning, RAG, evaluation, optimization, and deployment as disconnected techniques, Nolan Veyne shows you how to combine them into an evidence-driven engineering lifecycle.Inside, you'll learn how to: Select and baseline small language models based on domain capability, latency, memory, hardware, privacy, and costBuild governed, traceable domain datasets for training, validation, benchmarking, and evaluationApply domain adaptive pretraining, SFT, LoRA QLoRA, and preference optimization where they genuinely add valueMake informed RAG fine tuning decisions and combine retrieval with specialized models without confusing dynamic knowledge with learned behaviorBuild rigorous LLM evaluation workflows using golden sets, challenge tests, hallucination and faithfulness analysis, human assessment, and regression gatesApply quantization, distillation, batching, caching, and context management for practical LLM model optimizationEngineer reliable language model deployment across local, cloud, and edge environmentsDesign production APIs, model routing, verification, security, graceful degradation, and selective LLM fallbackDetect data drift, domain drift, retrieval failures, quality degradation, and rising production costsBuild controlled feedback loops, release gates, monitoring systems, and reusable engineering decision frameworksScale from one specialized model to a governed portfolio of production-ready AI capabilitiesThe question is no longer: "Which model is the most powerful?"It is: "What is the smallest and simplest AI system that can reliably satisfy the required quality and operational constraints?"Whether you're building an enterprise assistant, technical-support system, financial extraction model, engineering copilot, cybersecurity classifier, private knowledge interface, or another specialized AI application, this book gives you a practical path from model selection to production operation.Don't just fine-tune a model. Engineer the complete system around it.Get Domain-Specific AI Engineering by Nolan Veyne and learn how to build specialized language models that are measurable, efficient, optimized, deployable, and ready for real production workloads. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condition: new. Paperback. Your Zephyr application builds. It runs on the board. But is it actually ready for production?Getting firmware to work is only the beginning. Real embedded firmware development gets harder when a device leaves the development bench. Concurrency bugs emerge under load. Hardware revisions break assumptions. Memory and power become constrained. Networks disappear. Updates fail. And deployed devices may need to remain secure and maintainable for years.If you're learning embedded systems programming or working with real time operating systems, it's easy to know individual APIs while still struggling to understand how Kconfig, Devicetree, device drivers, threads, networking, storage, security, and firmware updates fit together.Zephyr RTOS Engineering bridges that gap.Rather than treating Zephyr as a collection of isolated features, this hands-on guide teaches you how to build portable, connected, secure, testable, and production-grade embedded systems. You'll learn not only how Zephyr mechanisms work, but when to use them, what trade-offs they introduce, and how to validate them.Inside, you'll learn how to: Master Zephyr development with West, CMake, Kconfig, Devicetree, boards, build artifacts, and reproducible workflows.Design reliable real-time firmware through RTOS concurrency programming with threads, scheduling, interrupts, work queues, synchronization, and message queues.Integrate real hardware using GPIO, I2C, SPI, UART, sensors, Devicetree, device drivers, and custom driver architectures.Build portable firmware across boards, peripherals, hardware revisions, and product variants.Engineer embedded IoT development with IP networking, sockets, MQTT, Bluetooth Low Energy, TLS, reconnection, and offline operation.Strengthen embedded systems security with threat modeling, device identity, credential protection, secure boot, and signed firmware.Master MCUboot firmware updates with flash partitions, DFU, OTA delivery, validation, rollback, recovery, and version management.Optimize constrained devices by measuring flash, RAM, stack, CPU, storage, logging, and power consumption.Build firmware testing and debugging workflows with Ztest, Twister, GDB, watchdogs, HIL testing, fault injection, and root-cause analysis.Build embedded CI/CD pipelines connecting automated builds, tests, HIL validation, security gates, signing, and controlled releases.This isn't a book about making one demo work. It is about developing the engineering judgment required to build firmware whose behavior can be reasoned about, measured, reproduced, diagnosed, secured, recovered, updated, ported, and maintained throughout the product lifecycle.If you're ready to move beyond Zephyr tutorials and start engineering embedded systems you can confidently take from development board to production, get your copy of Zephyr RTOS Engineering today. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.