Halversen bjorn (43 resultados)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Taschenbuch. Condición: Neu. Neuware.

Taschenbuch. Condición: Neu. Neuware.

Taschenbuch. Condición: Neu. Neuware.

Taschenbuch. Condición: Neu. Neuware.

Condición: New. Print on Demand.

Paperback. Condición: new. Paperback. The MERN stack remains one of the most powerful and productive JavaScript ecosystems for building real-time, feature-rich applications. MERN Stack Utilization by Experienced Engineers delivers the advanced implementation strategies and architectural patterns used by veteran developers to handle complex data persistence and secure authentication in production-grade chat applications. This professional guide explores battle-tested techniques for schema design, real-time data synchronization, scalable messaging architectures, authentication flows, session management, and security hardening specifically tailored to high-concurrency chat systems. Every chapter draws from real-world production experience, addressing challenges such as message history scaling, user presence, end-to-end encryption readiness, and seamless integration across the full MERN pipeline. Written exclusively for experienced fullstack JavaScript engineers, senior MERN developers, technical architects, and engineering leads who already build production applications, this book provides the depth and precision required to elevate chat applications from functional prototypes to robust, scalable, and secure platforms. Ready to master production-grade MERN patterns for data persistence and authentication in modern chat applications? Order your copy today and implement the same battle-tested approaches used by veteran engineers in high-traffic collaborative environments. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condición: New. Print on Demand.

Condición: New. Print on Demand.

Condición: New. Print on Demand.

Condición: New. Print on Demand.

Paperback. Condición: new. Paperback. Building an LLM prototype is easy. Engineering one that survives production is not. Enterprise Natural Language Processing with LLM is a practical architectural guide for software architects, AI engineers, and developers building production-ready NLP and LLM applications in Python.Move beyond simple prompts and demos to understand the systems, infrastructure, security, and engineering practices required to operate sophisticated generative AI at enterprise scale.Inside, you'll learn how to: Architect LLM-powered applications, including agentic and conversational systemsDesign advanced RAG architectures with hybrid search, re-ranking, query transformation, and graph-based contextManage prompts, context windows, token limits, versioning, and complex workflowsBuild and orchestrate multi-agent systems in Python with routing, human-in-the-loop validation, and execution tracingControl LLM costs through semantic caching, model routing, batching, and API optimizationFine-tune and self-host open-source models using LoRA, QLoRA, vLLM, TGI, and TensorRT-LLMDefend AI applications against prompt injection, jailbreaks, data leakage, and other security risksEstablish LLMOps pipelines for evaluation, A/B testing, model management, and continuous deploymentDesign model-agnostic architectures capable of adapting to evolving AI technologiesWhether you're designing an enterprise chatbot, RAG platform, autonomous AI workflow, or broader generative AI infrastructure, this book focuses on the architectural decisions that matter when prototypes become production systems. Build LLM applications that are scalable, secure, observable, and ready for the demands of real-world enterprise AI. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condición: New. Print on Demand.

Condición: New. Print on Demand.

Condición: New. Print on Demand.

Condición: New. Print on Demand.

Paperback. Condición: new. Paperback. What does it take to move from writing Python scripts to engineering production-ready AI systems? Professional Python for AI Engineering is a practical guide for software engineers and data scientists who want to use Python at a professional level for data science, machine learning, and deep learning applications.Rather than focusing on basic Python syntax, this book explores the advanced techniques needed to design, optimize, test, deploy, and maintain robust AI software.You'll explore how to: Build reproducible Python environments and manage dependencies for AI projectsOptimize memory usage, algorithms, data loading, and expensive computationsProcess massive datasets using multiprocessing, Dask, Polars, and out-of-core techniquesApply architectural patterns to create modular and maintainable machine learning systemsStructure PyTorch and TensorFlow projects for productionOptimize GPU utilization, mixed-precision training, distributed training, and hyperparametersBuild scalable and fault-tolerant model training pipelinesSerialize and deploy models with ONNX, TorchScript, REST APIs, gRPC, and containersTest, profile, debug, monitor, and continuously improve AI codebasesConnect data pipelines, feature stores, retraining workflows, telemetry, and security into end-to-end AI engineering systemsWhether you are developing machine learning pipelines, deep learning applications, or production AI infrastructure, this book focuses on the engineering practices that help turn experimental code into reliable software.Take your Python skills beyond scripting and into professional AI engineering. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…