Leveraging large language models (18 resultados)

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Taschenbuch. Condición: Neu. Generative AI-Driven Application Development with Java | Leveraging Large Language Models in Modern Java Applications | Satej Kumar Sahu | Taschenbuch | xxv | Englisch | 2026 | Apress | EAN 9798868816086 | Verantwortliche Person für die EU: APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

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

Condición: New. In English.

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

Paperback. Condición: New.

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

Condición: New. In English.

Condición: New.

Paperback. Condición: New.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This is the first hands-on guide that takes you from a simple Hello, LLM to production-ready microservices, all within the JVM. You ll integrate hosted models such as OpenAI s GPT-4o, run alternatives with Ollama or Jlama, and embed them in Spring Boot or Quarkus apps for cloud or on-pre deployment.You ll learn how prompt-engineering patterns, Retrieval-Augmented Generation (RAG), vector stores such as Pinecone and Milvus, and agentic workflows come together to solve real business problems. Robust test suites, CI/CD pipelines, and security guardrails ensure your AI features reach production safely, while detailed observability playbooks help you catch hallucinations before your users do. You ll also explore DJL, the future of machine learning in Java.This book delivers runnable examples, clean architectural diagrams, and a GitHub repo you can clone on day one. Whether you re modernizing a legacy platform or launching a green-field service, you ll have a roadmap for adding state-of-the-art generative AI without abandoning the language and ecosystem you rely on.What You Will LearnEstablish generative AI and LLM foundationsIntegrate hosted or local models using Spring Boot, Quarkus, LangChain4j, Spring AI, OpenAI, Ollama, and JlamaCraft effective prompts and implement RAG with Pinecone or Milvus for context-rich answersBuild secure, observable, scalable AI microservices for cloud or on-prem deploymentTest outputs, add guardrails, and monitor performance of LLMs and applicationsExplore advanced patterns, such as agentic workflows, multimodal LLMs, and practical image-processing use casesWho This Book Is ForJava developers, architects, DevOps engineers, and technical leads who need to add AI features to new or existing enterprise systems. Data scientists and educators will also appreciate the code-first, Java-centric approach.…

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - As the digital landscape evolves, the growing threat of cyberattacks has prompted the need for more advanced security measures. One of the most promising developments in cybersecurity is the integration of large language models (LLMs) with quantum-aware systems. These AI-powered models, capable of processing data and recognizing complex patterns, play a pivotal role in identifying vulnerabilities, predicting threats, and enhancing the resilience of security infrastructures. In quantum computing, LLMs offer new opportunities to stay ahead of cyber threats by simulating attack strategies and developing adaptive defense mechanisms. By harnessing the power of these tools, cybersecurity professionals can address current challenges while preparing for an era of quantum-enabled cyber threats. Leveraging Large Language Models for Quantum-Aware Cybersecurity explores the convergence of LLMs, cybersecurity, and quantum computing, providing an in-depth analysis of how these fields are being integrated to tackle emerging challenges in the digital security landscape. It covers foundational concepts, cutting-edge research, and practical applications, demonstrating how LLMs can be leveraged alongside quantum technologies to enhance threat detection, automate incident response, and build quantum-resilient security frameworks. This book covers topics such as artificial intelligence, computer engineering, natural language processing, and is a useful resource for computer engineers, security professionals, scientists, academicians, and researchers.…

Buch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - As the digital landscape evolves, the growing threat of cyberattacks has prompted the need for more advanced security measures. One of the most promising developments in cybersecurity is the integration of large language models (LLMs) with quantum-aware systems. These AI-powered models, capable of processing data and recognizing complex patterns, play a pivotal role in identifying vulnerabilities, predicting threats, and enhancing the resilience of security infrastructures. In quantum computing, LLMs offer new opportunities to stay ahead of cyber threats by simulating attack strategies and developing adaptive defense mechanisms. By harnessing the power of these tools, cybersecurity professionals can address current challenges while preparing for an era of quantum-enabled cyber threats. Leveraging Large Language Models for Quantum-Aware Cybersecurity explores the convergence of LLMs, cybersecurity, and quantum computing, providing an in-depth analysis of how these fields are being integrated to tackle emerging challenges in the digital security landscape. It covers foundational concepts, cutting-edge research, and practical applications, demonstrating how LLMs can be leveraged alongside quantum technologies to enhance threat detection, automate incident response, and build quantum-resilient security frameworks. This book covers topics such as artificial intelligence, computer engineering, natural language processing, and is a useful resource for computer engineers, security professionals, scientists, academicians, and researchers.…