Veyne nolan (37 risultati)

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Condizione: 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.…

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Taschenbuch. Condizione: Neu. Neuware.

Taschenbuch. Condizione: Neu. Neuware.

Paperback. Condizione: 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. Condizione: Neu. Neuware.

Taschenbuch. Condizione: Neu. Neuware.

Taschenbuch. Condizione: 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.…

Condizione: New. Print on Demand.

Condizione: New. Print on Demand.

Taschenbuch. Condizione: Neu. Neuware.

Condizione: New. Print on Demand.

Condizione: New. Print on Demand.

Condizione: New. Print on Demand.

Condizione: New. Print on Demand.

Condizione: New. Print on Demand.

Condizione: New. Print on Demand.

Paperback. Condizione: 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.…

Condizione: New. Print on Demand.

Paperback. Condizione: new. Paperback. Modern generative AI is powerful-but understanding why diffusion models, score-based generative models, stochastic differential equations, and flow matching actually work is another challenge entirely.You may know how to train a neural network or follow a diffusion models PyTorch tutorial, yet still wonder: Why does adding noise create a generative process? Where does the reverse-time SDE come from? What does a score function represent? How are Langevin dynamics, stochastic thermodynamics, machine learning, diffusion, and flow matching connected?Generative AI Through Stochastic Dynamics bridges that gap.Rather than treating modern generative models as disconnected architectures, this hands-on guide develops them through one powerful idea: generative AI as the transformation of probability distributions through stochastic and deterministic dynamics. You will build the generative AI mathematics needed to understand how probability, energy, noise, scores, and velocity fields become practical algorithms.From probability and Brownian motion, you will progress through Langevin dynamics, stochastic differential equations in machine learning, Fokker-Planck equations, diffusion models, score-based generative models, optimal transport, normalizing flows, flow matching generative models, stochastic control, and complete generative systems.Inside, you will learn how to: Build strong foundations in generative AI mathematics, probability, entropy, KL divergence, Brownian motion, and stochastic dynamicsUnderstand stochastic thermodynamics for machine learning, including energy, free energy, equilibrium, nonequilibrium dynamics, and entropy productionDerive and simulate Langevin dynamics and stochastic differential equations (SDEs)Understand diffusion models from first principles and build a diffusion model in PyTorch from scratchMaster score-based generative models, score matching, VP and VE processes, reverse-time SDEs, and probability-flow ODEsExplore normalizing flows for machine learning, neural ODEs, optimal transport, flow matching, and conditional flow matchingConnect VAEs, energy-based models, MCMC, Langevin sampling, stochastic control, and Schroedinger bridgesBuild reproducible Python and PyTorch experiments and evaluate distribution quality, numerical accuracy, NFE, latency, and sampling performanceDiagnose mathematical, numerical, training, and sampling failures instead of relying on trial and errorA defining feature of the book is its careful treatment of stochastic thermodynamics and machine learning. You will explore rigorous connections involving probability currents, stochastic paths, time reversal, entropy, energy, and nonequilibrium dynamics while learning to distinguish genuine physical thermodynamics from mathematical analogies used in generative computation.Whether you are an advanced student, machine-learning engineer, AI researcher, computational scientist, or applied mathematician, this book gives you a deeper framework for understanding the mathematics and computation behind modern generative AI.Stop treating diffusion models, scores, flows, and SDEs as isolated techniques. Learn the principles that connect them-and build generative models from the mathematics upward.Understand the physics. Derive the mathematics. Implement the dynamics. Build the generative models. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condizione: New. Print on Demand.

Paperback. Condizione: 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 our UK warehouse or from our Australian or US warehouses, depending on stock availability.…