generative ai driven application development java (17 résultats)

Etat : New.

Paperback. Etat : New.

Paperback. Etat : New.

Paperback. Etat : New.

Taschenbuch. Etat : 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.

Paperback. Etat : New.

Etat : New. Brand New. Soft Cover International Edition. Different ISBN and Cover Image. Priced lower than the standard editions which is usually intended to make them more affordable for students abroad. The core content of the book is generally the same as the standard edition. The country selling restrictions may be printed on the book but is no problem for the self-use. This Item maybe shipped from US or any other country as we have multiple locations worldwide.

Etat : New. Brand New, Softcover edition. This item may ship from the US or our Overseas warehouse depending on your location and stock availability.

Paperback. Etat : new. Paperback. This is the first hands-on guide that takes you from a simple Hello, LLM to production-ready microservices, all within the JVM. Youll integrate hosted models such as OpenAIs GPT-4o, run alternatives with Ollama or Jlama, and embed them in Spring Boot or Quarkus apps for cloud or on-pre deployment.Youll 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. Youll 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 youre modernizing a legacy platform or launching a green-field service, youll have a roadmap for adding state-of-the-art generative AI without abandoning the languageand ecosystemyou 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 cases Who 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

PAP. Etat : New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Etat : New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 698 pp. Englisch.

Etat : New.

Paperback. Etat : new. Paperback. This is the first hands-on guide that takes you from a simple Hello, LLM to production-ready microservices, all within the JVM. Youll integrate hosted models such as OpenAIs GPT-4o, run alternatives with Ollama or Jlama, and embed them in Spring Boot or Quarkus apps for cloud or on-pre deployment.Youll 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. Youll 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 youre modernizing a legacy platform or launching a green-field service, youll have a roadmap for adding state-of-the-art generative AI without abandoning the languageand ecosystemyou 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 cases Who 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. 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. Etat : new. Paperback. This is the first hands-on guide that takes you from a simple Hello, LLM to production-ready microservices, all within the JVM. Youll integrate hosted models such as OpenAIs GPT-4o, run alternatives with Ollama or Jlama, and embed them in Spring Boot or Quarkus apps for cloud or on-pre deployment.Youll 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. Youll 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 youre modernizing a legacy platform or launching a green-field service, youll have a roadmap for adding state-of-the-art generative AI without abandoning the languageand ecosystemyou 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 cases Who 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. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 724 pp. Englisch.

Taschenbuch. Etat : 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.