Nexley Devlin (22 results)

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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - PyTorch Systems Engineering is a deep, architecture-driven exploration of PyTorch as a modern execution platform for large-scale AI systems. It moves beyond model building and API usage to focus on the internal mechanisms that define performance, scalability, and reliability in production deep learning workloads.As AI systems grow in complexity-from large language models to distributed multimodal pipelines-the challenges shift away from model design and toward systems engineering: how tensors are executed across heterogeneous hardware, how autograd constructs and traverses dynamic computation graphs, how GPU memory is allocated and optimized under pressure, and how distributed training systems maintain correctness and efficiency at scale.This book treats PyTorch not as a library, but as a full runtime system composed of tightly integrated subsystems: a dynamic execution engine, a tensor computation backend, a compiler pipeline, a distributed coordination layer, and a GPU-accelerated memory management system.Throughout the book, readers will gain a practical understanding of how PyTorch actually behaves under production workloads, including its performance characteristics, internal abstractions, and failure modes.You will learn how to reason about deep learning systems in terms of execution flow, memory behavior, and infrastructure constraints rather than isolated model architectures.Key topics include:PyTorch runtime architecture and execution modelTensor memory layout, storage semantics, and GPU data movementAutograd internals and dynamic computation graph constructionCUDA execution, kernel optimization, and GPU performance tuningMixed precision training and numerical stability in large modelsCompiler pipelines including graph capture and kernel fusion systemsDistributed training architectures such as DDP and FSDPTransformer and large language model training systemsMemory optimization strategies for large-scale workloadsProduction inference pipelines and deployment architecturesPyTorch extensibility through C++ extensions and CUDA kernelsDebugging distributed systems and runtime failure modesBy the end of this book, readers will be able to design, optimize, and operate production-grade AI systems built on PyTorch, with a clear understanding of the tradeoffs that govern performance, scalability, and system reliability.This is not an introductory guide to machine learning. It is a systems engineering manual for building and scaling modern AI infrastructure with PyTorch.…

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Apache Spark is not just a data processing framework-it is a distributed execution system built on deep principles of cluster computing, DAG-based scheduling, memory-aware computation, and fault-tolerant design.This book provides a rigorous, systems-level examination of Spark as a production-grade distributed execution engine. It moves beyond APIs, tutorials, and surface-level usage patterns to expose the internal mechanisms that govern how Spark actually executes workloads at scale.Designed for experienced engineers working in distributed systems, backend infrastructure, and data platform engineering, this book dissects Spark as an architectural system rather than a development tool.Inside, you will explore how Spark transforms high-level computations into distributed execution graphs, how it schedules and coordinates work across clusters, and how it manages the complexity of large-scale data movement in cloud-native environments.Key areas covered include:Internal architecture of Spark's driver, executors, and cluster coordination modelDAG construction, stage decomposition, and task scheduling mechanicsShuffle architecture, data movement patterns, and network bottlenecksMemory management, execution optimization, and JVM runtime behaviorFault tolerance through lineage reconstruction and retry semanticsQuery execution via Spark SQL, Catalyst optimizer, and Tungsten engineStructured Streaming and incremental computation modelsPerformance bottlenecks, skew handling, and production tuning strategiesCloud-native execution on object storage systems and KubernetesIntegration with modern lakehouse ecosystems such as Delta Lake, Iceberg, and HudiRather than presenting Spark as a tool to be used, this book treats it as a distributed systems case study-revealing how large-scale data infrastructure is engineered, optimized, and operated under real production constraints.By the end, readers will understand not only how Spark works, but why its architecture is designed the way it is, what trade-offs shape its execution model, and how it fits into the broader evolution of modern distributed data platforms.This is a book for engineers who build systems, not just pipelines.…

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Modern AI systems require far more than access to pretrained models. Building reliable machine learning platforms requires an understanding of model architecture, data pipelines, distributed training, inference infrastructure, lifecycle management, security, observability, and the operational economics of GPU-based workloads.This book provides a production-focused examination of the technologies and engineering practices used to build modern AI systems with the Hugging Face ecosystem. It moves beyond introductory model usage to explore how models, datasets, training workloads, inference runtimes, and application services fit together within scalable engineering environments.The book examines transformer architectures, model repositories, dataset engineering, advanced inference, parameter-efficient fine-tuning, distributed training, embeddings, retrieval-augmented generation, multimodal systems, and AI application infrastructure. It also addresses the operational concerns that emerge when these capabilities move from experimentation into enterprise environments.Particular attention is given to production architecture: model lineage, artifact management, evaluation pipelines, CI/CD for machine learning, deployment automation, GPU infrastructure, observability, cost optimization, autoscaling, multi-region deployments, and enterprise governance. The discussion considers the tradeoffs behind architectural decisions rather than treating AI platforms as collections of isolated tools.Topics covered include:¿ Hugging Face platform and repository architecture¿ Transformer model architecture and runtime considerations¿ Model packaging, versioning, and artifact management¿ Dataset engineering, streaming, validation, and lineage¿ High-performance inference and GPU optimization¿ Full-parameter and parameter-efficient fine-tuning¿ Distributed training with modern acceleration strategies¿ Embedding and semantic retrieval architectures¿ Production RAG system design¿ Multimodal AI workloads¿ AI applications and deployment infrastructure¿ Model evaluation, benchmarking, and observability¿ Security, governance, access control, and compliance¿ ML CI/CD and automated model promotion¿ Enterprise AI platform engineering and FinOps¿ Scaling inference services across heterogeneous infrastructureDesigned for AI engineers, machine learning engineers, MLOps professionals, platform engineers, solution architects, and enterprise technology leaders, the material emphasizes architectural reasoning, implementation tradeoffs, operational reliability, and production-scale engineering.Rather than presenting AI development as a sequence of isolated experiments, this book examines the systems required to turn models and machine learning workflows into durable, observable, secure, and scalable production services. …

Taschenbuch. Condition: Neu. PyTorch Systems Engineering | Architecture, Runtime Internals, and Scalable AI Infrastructure for Production Deep Learning | Devlin Nexley | Taschenbuch | Englisch | 2026 | Devlin Nexley | EAN 9788675164159 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand. …

Taschenbuch. Condition: Neu. Apache Spark for Distributed Systems Engineers | Devlin Nexley | Taschenbuch | Englisch | 2026 | Devlin Nexley | EAN 9789326137003 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand. …

Taschenbuch. Condition: Neu. Hugging Face Architecture | Devlin Nexley | Taschenbuch | Englisch | 2026 | Devlin Nexley | EAN 9781053477841 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand.