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LLMs in Enterprise
LLMs in Enterprise

LLMs in Enterprise: Design strategies for large language model development, design patterns and best practices

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Profile Icon Ahmed Menshawy Profile Icon Mahmoud Fahmy
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Early Access
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eBook Apr 2025 1st Edition
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Free Trial
Arrow left icon
Profile Icon Ahmed Menshawy Profile Icon Mahmoud Fahmy
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Early Access
Free Trial
eBook Apr 2025 1st Edition
Subscription
Free Trial
Subscription
Free Trial

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LLMs in Enterprise

LLMs in Enterprise: Design strategies for large language model development, design patterns and best practices

Welcome to Packt Early Access. We’re giving you an exclusive preview of this book before it goes on sale. It can take many months to write a book, but our authors have cutting-edge information to share with you today. Early Access gives you an insight into the latest developments by making chapter drafts available. The chapters may be a little rough around the edges right now, but our authors will update them over time.

You can dip in and out of this book or follow along from start to finish; Early Access is designed to be flexible. We hope you enjoy getting to know more about the process of writing a Packt book.

  1. Chapter 1: Introduction to Large Language Models (LLMs)
  2. Chapter 2: LLMs in Enterprise: Applications, Challenges, and Design Patterns
  3. Chapter 3: Data and Training in Foundation Models
  4. Chapter 4: Fine-Tuning and Retrieval-Augmented Generation (RAG) Patterns
  5. Chapter 5: Customizing Contextual LLMs Patterns
  6. Chapter 6: Evaluation Patterns
  7. Chapter 7: Data Strategy for LLMs
  8. Chapter 8: Model Deployment
  9. Chapter 9: Accelerated and Optimized Inferencing Patterns
  10. Chapter 10: LLMs in Production
  11. Chapter 11: RAG 2.0: Beyond Mainstream RAG
  12. Chapter 12: Connected LLMs Pattern
  13. Chapter 13: Responsible AI in LLMs
  14. Chapter 14: Emerging Trends and Multimodality
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Key benefits

  • Design patterns for LLMs and how they can be applied to solve real-world enterprise problems 
  • Strategies for effectively scaling and deploying LLMs in complex enterprise environments 
  • Fine-tuning and optimizing LLMs to achieve better performance and more relevant results.  
  • Staying ahead of the curve by exploring emerging trends and advancements in LLM technologies.

Description

The integration of Large Language Models (LLMs) into enterprise applications marks a significant advancement in how businesses leverage AI for enhanced decision-making and operational efficiency. This book is an essential guide for professionals seeking to integrate LLMs within their enterprise applications. "LLMs in Enterprise" not only demystifies the complexity behind LLM deployment but also provides a structured approach to enhancing decision-making and operational efficiency with AI. Starting with an introduction to the foundational concepts of LLMs, the book swiftly moves to practical applications, emphasizing real-world challenges and solutions. It covers a range of topics from data strategies. We explore various design patterns that are particularly effective in optimizing and deploying LLMs in enterprise environments. From fine-tuning strategies to advanced inferencing patterns, the book provides a toolkit for harnessing the power of LLMs to solve complex challenges and drive innovation in business processes. By the end of this book, you will have a deep understanding of various design patterns for LLMs and how to implement these patterns to enhance the performance and scalability of their Generative AI solutions.

Who is this book for?

​This book targets a diverse group of professionals who are interested in understanding and implementing advanced design patterns for Large Language Models (LLMs) within their enterprise applications, including:  ​​ AI and ML Researchers who are looking into practical applications of LLMs  Data Scientists and ML Engineers who design and implement large-scale Generative AI solutions Enterprise Architects and Technical Leaders who oversee the integration of AI technologies into business processes Software Developers who work on developing scalable Generative AI-powered applications.

What you will learn

  • Design patterns for integrating LLMs into enterprise applications, enhancing both efficiency and scalability 
  • Overcome common scaling and deployment challenges associated with LLMs 
  • Fine-tuning techniques and RAG approaches to improve the effectiveness and efficiency of LLMs
  • Emerging trends and advancements including multimodality and beyond
  • Optimize LLM performance through customized contextual models, advanced inferencing engines, and robust evaluation patterns
  • Ensure fairness, transparency, and accountability in AI applications

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Apr 30, 2025
Edition : 1st
Language : English
ISBN-13 : 9781836203063
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Languages :
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Product feature icon 50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.
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Product Details

Publication date : Apr 30, 2025
Edition : 1st
Language : English
ISBN-13 : 9781836203063
Category :
Languages :
Concepts :

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Table of Contents

5 Chapters
LLMs in Enterprise: Design strategies for large language model development, design patterns and best practices Chevron down icon Chevron up icon
1 Introduction to Large Language Models (LLMs) Chevron down icon Chevron up icon
2 LLMs in Enterprise: Applications, Challenges, and Design Patterns Chevron down icon Chevron up icon
4 Fine-Tuning and Retrieval-Augmented Generation(RAG) Strategies Chevron down icon Chevron up icon
5 Customizing Contextual LLMs Chevron down icon Chevron up icon
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