Carter Jazper (27 results)

Paperback or Softback. Condition: New. New Manager AI Handbook. Book.

Condition: New.

Condition: New.

Condition: New.

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

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

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

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

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

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

Paperback. Condition: New.

Condition: New.

Paperback. Condition: Brand New. 471 pages. 6.00x1.07x9.00 inches. In Stock.

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Taschenbuch. Condition: Neu. Neuware.

Taschenbuch. Condition: Neu. Neuware.

Paperback. Condition: New.

Paperback. Condition: new. Paperback. Every day, satellite images appear in news broadcasts, insurance assessments, agricultural dashboards, and disaster briefings. Most people have seen them. Far fewer can actually read them. There is a gap between seeing a satellite image and understanding what it is, how it was made, and what decisions ride on it. That gap persists because the books explaining geospatial intelligence are written for engineers and remote sensing specialists. This book was written for everyone else. No equations. No code. No background in geography or physics required. Just clear, plain-English explanations of one of the most powerful technologies reshaping how organizations observe and act on the planet. Inside this book, readers will learn how to: Read satellite imagery with genuine comprehension: what wavelengths reveal, why image colors are often false, and what trained analysts see that casual observers missGrasp the full electromagnetic spectrum and why invisible wavelengths like near-infrared, thermal infrared, and synthetic aperture radar reveal what no ordinary camera can captureTrace raw sensor data through the complete processing pipeline from orbit to analysis-ready imagery, with enough depth to evaluate vendor claims and data quality assurancesApply change detection, vegetation indices, and land cover classification to real decisions in agriculture, emergency management, and urban developmentBuild geospatial literacy applicable to agriculture, climate science, emergency response, investigative journalism, and national security using the same conceptual toolkitEvaluate AI-enhanced geospatial products critically and understand where confident-looking machine learning outputs can mislead decision-makersNavigate free tools and global archives including Google Earth Engine, QGIS, Landsat, and the Copernicus Sentinel program, and know exactly where to beginAsk sharp, informed questions of geospatial vendors, data science teams, and government briefers rather than accepting map outputs at face valueUnderstand GEOINT tradecraft, open-source satellite intelligence, and the ethical dimensions of near-continuous overhead surveillance Geospatial intelligence no longer belongs exclusively to defense agencies and research institutions. It now shapes insurance underwriting, precision farming, emergency operations, investigative journalism, commodity trading, and city management. Satellite constellations image the entire Earth daily. Free archives from Landsat and the Copernicus program stretch back decades. Cloud platforms put global analysis within reach of anyone with a question. The bottleneck is no longer access. It is the conceptual literacy to use what is already available. Nine tightly structured chapters move from the physics of how satellites observe the Earth to the practice of AI-driven analysis, anchored throughout in vivid real-world scenarios: wildfire response, crop stress detection, flood mapping, GEOINT tradecraft, and humanitarian operations. Every technical term is defined precisely where it first appears. No glossary required. Whether you are a data scientist encountering spatial data for the first time, a journalist evaluating satellite evidence, a manager vetting a geospatial vendor, or simply a curious professional, this book delivers geospatial literacy that is immediately and practically useful. 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. Distributed machine learning systems fail in ways single-node systems never do. A 1024-GPU training job stalls for four hours while every worker reports healthy; gradient synchronization deadlocks leave no stack trace and no alert. A serving cluster absorbs a traffic spike, then silently doubles inference cost because the KV cache policy was tuned for a model half the size. Checkpoint corruption surfaces only after twelve hours of resumed training. These are the predictable failure modes of distributed systems, and the teams that ship reliable distributed ML design against them with patterns that hold across frameworks, clouds, and model scales.Inside this book, readers will learn how to: Design parallelism strategies that fit workload shape and hardware, selecting among data, tensor, pipeline, and expert axes based on architecture, memory budget, and interconnect topology.Tune gradient synchronization and sharding applying ZeRO, FSDP, and pipeline schedules to keep accelerator utilization high without amplifying communication overhead as cluster size grows.Build fault-tolerant training pipelines with checkpoint strategies, elastic cluster patterns, and spot instance management that recover from mid-run hardware failures without restarting from epoch zero.Operate inference at scale using continuous batching, paged attention, and KV cache management to maximize throughput and meet latency SLOs under variable load.Instrument distributed jobs for observability tracing per-rank metrics, gradient norms, and communication timings so silent failures surface before consuming days of compute budget.Manage multi-tenant clusters securely with workload isolation, quota enforcement, and cost attribution that keep shared GPU infrastructure safe and financially accountable.Apply LLM and foundation model patterns for distributed pre-training, RLHF infrastructure, and large-scale inference that generalize across architectures as hardware generations turn over.Assess platform maturity using the book's maturity model to locate gaps in reliability, cost efficiency, and operational readiness across the distributed ML stack.Frameworks rotate; the parallelism decisions, synchronization tradeoffs, and fault-tolerance designs that determine whether a distributed ML system works at scale do not. As foundation models grow larger and serving loads grow steeper, the distance between teams that reason in patterns and teams that copy configurations will only widen.The book is organized in four parts: Foundations, covering parallelism patterns, data sharding, I/O, and orchestration; Training at Scale, addressing fault-tolerant training, checkpoint management, and spot scheduling; Serving and Operations, covering inference architecture, cost control, observability, and multi-tenant security; and Frontier Patterns, applying everything to LLMs and foundation models and closing with end-to-end case studies and a full platform synthesis.This book is for ML architects who design distributed systems others depend on, ML engineers and data engineers who build and operate them, and technical team leads who set reliability and cost standards, with platform and SRE engineers as a strong secondary audience. Every chapter opens with a production incident scenario, teaches canonical patterns by name, and closes with a checklist the team can apply immediately. Readers finish with the vocabulary, playbook, and pattern library to ship reliable distributed ML systems with confidence. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condition: New. Print on Demand.

Condition: New. PRINT ON DEMAND.

Condition: New. Print on Demand.

Condition: New. PRINT ON DEMAND.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Stepping into management in the AI era isn't just a promotion-it's an identity shift. New managers quickly discover that the skills that made them exceptional individual contributors no longer define success. As your book states, 'The skills and identity that earned you this promotion are no longer the ones that will make you successful in it.' This handbook gives new and aspiring leaders the clarity, confidence, and systems they need to thrive in a workplace transformed by AI, hybrid teams, and accelerating expectations.The New Manager AI Handbook is the definitive guide for navigating the dual challenge of leading people while leveraging AI to amplify-not replace-human judgment. Drawing from real scenarios, research-backed frameworks, and modern leadership psychology, it equips readers to build trust, communicate with impact, delegate effectively, and make datäinformed decisions without losing the human core of leadership.You'll learn how to transition from doing the work to enabling the work-shifting from personal output to team outcomes. You'll understand why new managers often feel disoriented, why old habits pull you back into execution, and how to build the leadership foundation your team needs. As your manuscript explains, 'Management is the set of processes. leadership is the set of behaviors. great managers develop both.'Inside, readers will discover:A clear roadmap for the first 30, 60, and 90 days-including how to listen deeply, establish credibility, and set the tone for your leadership brand.A modern communication system for hybrid and remote teams, ensuring clarity, alignment, and psychological safety.A complete delegation framework that transforms overwhelmed managers into strategic leaders while developing team capability.AI¿powered workflows that reclaim hours each week by automating administrative load so managers can reinvest time into coaching, decision¿making, and relationship¿building.Research¿driven insights on emotional intelligence, trust, conflict resolution, and performance management.A practical blueprint for building a high¿performing, future¿ready team that adapts to change, embraces innovation, and grows stronger through uncertainty.This book doesn't ask managers to become technologists. It shows them how to become better leaders-leaders who use AI responsibly, ethically, and strategically to elevate human potential. As your text emphasizes, 'AI is a force multiplier for managers who already know what good looks like.'Whether you're newly promoted, preparing for leadership, or modernizing your approach, this handbook gives you the tools to lead with clarity, confidence, and impact in a rapidly evolving workplace.If you want to become the kind of manager your team deserves-and your organization relies on-this is your playbook for the AI¿powered decade ahead.…

Paperback. Condition: new. Paperback. Every day, satellite images appear in news broadcasts, insurance assessments, agricultural dashboards, and disaster briefings. Most people have seen them. Far fewer can actually read them. There is a gap between seeing a satellite image and understanding what it is, how it was made, and what decisions ride on it. That gap persists because the books explaining geospatial intelligence are written for engineers and remote sensing specialists. This book was written for everyone else. No equations. No code. No background in geography or physics required. Just clear, plain-English explanations of one of the most powerful technologies reshaping how organizations observe and act on the planet. Inside this book, readers will learn how to: Read satellite imagery with genuine comprehension: what wavelengths reveal, why image colors are often false, and what trained analysts see that casual observers missGrasp the full electromagnetic spectrum and why invisible wavelengths like near-infrared, thermal infrared, and synthetic aperture radar reveal what no ordinary camera can captureTrace raw sensor data through the complete processing pipeline from orbit to analysis-ready imagery, with enough depth to evaluate vendor claims and data quality assurancesApply change detection, vegetation indices, and land cover classification to real decisions in agriculture, emergency management, and urban developmentBuild geospatial literacy applicable to agriculture, climate science, emergency response, investigative journalism, and national security using the same conceptual toolkitEvaluate AI-enhanced geospatial products critically and understand where confident-looking machine learning outputs can mislead decision-makersNavigate free tools and global archives including Google Earth Engine, QGIS, Landsat, and the Copernicus Sentinel program, and know exactly where to beginAsk sharp, informed questions of geospatial vendors, data science teams, and government briefers rather than accepting map outputs at face valueUnderstand GEOINT tradecraft, open-source satellite intelligence, and the ethical dimensions of near-continuous overhead surveillance Geospatial intelligence no longer belongs exclusively to defense agencies and research institutions. It now shapes insurance underwriting, precision farming, emergency operations, investigative journalism, commodity trading, and city management. Satellite constellations image the entire Earth daily. Free archives from Landsat and the Copernicus program stretch back decades. Cloud platforms put global analysis within reach of anyone with a question. The bottleneck is no longer access. It is the conceptual literacy to use what is already available. Nine tightly structured chapters move from the physics of how satellites observe the Earth to the practice of AI-driven analysis, anchored throughout in vivid real-world scenarios: wildfire response, crop stress detection, flood mapping, GEOINT tradecraft, and humanitarian operations. Every technical term is defined precisely where it first appears. No glossary required. Whether you are a data scientist encountering spatial data for the first time, a journalist evaluating satellite evidence, a manager vetting a geospatial vendor, or simply a curious professional, this book delivers geospatial literacy that is immediately and practically useful. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Condition: new. Paperback. Distributed machine learning systems fail in ways single-node systems never do. A 1024-GPU training job stalls for four hours while every worker reports healthy; gradient synchronization deadlocks leave no stack trace and no alert. A serving cluster absorbs a traffic spike, then silently doubles inference cost because the KV cache policy was tuned for a model half the size. Checkpoint corruption surfaces only after twelve hours of resumed training. These are the predictable failure modes of distributed systems, and the teams that ship reliable distributed ML design against them with patterns that hold across frameworks, clouds, and model scales.Inside this book, readers will learn how to: Design parallelism strategies that fit workload shape and hardware, selecting among data, tensor, pipeline, and expert axes based on architecture, memory budget, and interconnect topology.Tune gradient synchronization and sharding applying ZeRO, FSDP, and pipeline schedules to keep accelerator utilization high without amplifying communication overhead as cluster size grows.Build fault-tolerant training pipelines with checkpoint strategies, elastic cluster patterns, and spot instance management that recover from mid-run hardware failures without restarting from epoch zero.Operate inference at scale using continuous batching, paged attention, and KV cache management to maximize throughput and meet latency SLOs under variable load.Instrument distributed jobs for observability tracing per-rank metrics, gradient norms, and communication timings so silent failures surface before consuming days of compute budget.Manage multi-tenant clusters securely with workload isolation, quota enforcement, and cost attribution that keep shared GPU infrastructure safe and financially accountable.Apply LLM and foundation model patterns for distributed pre-training, RLHF infrastructure, and large-scale inference that generalize across architectures as hardware generations turn over.Assess platform maturity using the book's maturity model to locate gaps in reliability, cost efficiency, and operational readiness across the distributed ML stack.Frameworks rotate; the parallelism decisions, synchronization tradeoffs, and fault-tolerance designs that determine whether a distributed ML system works at scale do not. As foundation models grow larger and serving loads grow steeper, the distance between teams that reason in patterns and teams that copy configurations will only widen.The book is organized in four parts: Foundations, covering parallelism patterns, data sharding, I/O, and orchestration; Training at Scale, addressing fault-tolerant training, checkpoint management, and spot scheduling; Serving and Operations, covering inference architecture, cost control, observability, and multi-tenant security; and Frontier Patterns, applying everything to LLMs and foundation models and closing with end-to-end case studies and a full platform synthesis.This book is for ML architects who design distributed systems others depend on, ML engineers and data engineers who build and operate them, and technical team leads who set reliability and cost standards, with platform and SRE engineers as a strong secondary audience. Every chapter opens with a production incident scenario, teaches canonical patterns by name, and closes with a checklist the team can apply immediately. Readers finish with the vocabulary, playbook, and pattern library to ship reliable distributed ML systems with confidence. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Taschenbuch. Condition: Neu. New Manager AI Handbook | Jazper Carter | Taschenbuch | Englisch | 2025 | Cybersoft Publishers | EAN 9781972752593 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.