Enterprise Natural Language Processing with LLM (Paperback)
Language: English
Published by Independently Published, 2026
Series: Book 4 of 4 - The Enterprise Data Science Mastery Series
- Softcover
- New
Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail
AbeBooks seller since October 12, 2005
Condition: New
£ 17.53
Quantity: 1 available
Add to basketItem description from seller
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.…
Seller Inventory # 9798170565047
- Title
- Enterprise Natural Language Processing with LLM (Paperback)
- Author
- Bjorn Halversen
- Publisher
- Independently Published
- Publication year
- 2026
- Condition
- new
- Binding
- Paperback
- Language
- English
- ISBN 13
- 9798170565047
- Series
- Book 4 of 4: The Enterprise Data Science Mastery Series
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 systems
- Design advanced RAG architectures with hybrid search, re-ranking, query transformation, and graph-based context
- Manage prompts, context windows, token limits, versioning, and complex workflows
- Build and orchestrate multi-agent systems in Python with routing, human-in-the-loop validation, and execution tracing
- Control LLM costs through semantic caching, model routing, batching, and API optimization
- Fine-tune and self-host open-source models using LoRA, QLoRA, vLLM, TGI, and TensorRT-LLM
- Defend AI applications against prompt injection, jailbreaks, data leakage, and other security risks
- Establish LLMOps pipelines for evaluation, A/B testing, model management, and continuous deployment
- Design model-agnostic architectures capable of adapting to evolving AI technologies
Build LLM applications that are scalable, secure, observable, and ready for the demands of real-world enterprise AI.
"Synopsis" may belong to another edition of this title.
Grand Eagle Retail
Bensenville, IL, U.S.A.
AbeBooks seller since October 12, 2005
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