Generative Ai Driven Application Development by Sahu Satej (11 results)
- Softcover
Seller: California Books, Miami, FL, U.S.A.California Books
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- Softcover
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- Softcover
Seller: Rarewaves.com USA, London, LONDO, United KingdomRarewaves.com USA
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Paperback. Condition: New. 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.Y…ou'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 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.
- Softcover
Seller: PBShop.store UK, Fairford, GLOS, United KingdomPBShop.store UK
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PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.
- Softcover
Seller: Rarewaves USA United, OSWEGO, IL, U.S.A.Rarewaves USA United
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Paperback. Condition: New. 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.Y…ou'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 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.
- Softcover
Seller: preigu, Osnabrück, Germanypreigu
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Taschenbuch. Condition: 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 Pl…atz 3, 14197 Berlin, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
- Softcover
Seller: Rarewaves.com UK, London, United KingdomRarewaves.com UK
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Paperback. Condition: New. 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.Y…ou'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 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.
- Softcover
- Print on Demand
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condition: 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.
- Softcover
- Print on Demand
Seller: CitiRetail, Stevenage, United KingdomCitiRetail
Contact seller5-star sellerCondition: New
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Paperback. Condition: 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 deplo…yment.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.
- Softcover
- Print on Demand
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germanybuchversandmimpf2000
Contact seller5-star sellerCondition: New
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Taschenbuch. Condition: 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 t…hem in Spring Boot or Quarkus apps for cloud or on-pre deployment.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 724 pp. Englisch.
- Softcover
- Print on Demand
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Taschenbuch. Condition: 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.
