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

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Ahmed Menshawy Mahmoud Fahmy

LLMs in Enterprise

Design strategies, patterns, and best practices for large language model development

Ahmed Menshawy, Mahmoud Fahmy

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Paperback

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English

Packt Publishing Limited
19 September 2025

Project management software; Natural language & machine translation; Neural networks & fuzzy systems

Integrate large language models into your enterprise applications with advanced strategies that drive transformation

Key Features

Explore design patterns for applying LLMs to solve real-world enterprise problems Learn strategies for scaling and deploying LLMs in complex environments Get more relevant results and improve performance by fine-tuning and optimizing LLMs Purchase of the print or Kindle book includes a free PDF eBook

Book DescriptionThe integration of large language models (LLMs) into enterprise applications is transforming how businesses use AI to drive smarter decisions and efficient operations. LLMs in Enterprise is your practical guide to bringing these capabilities into real-world business contexts. It demystifies the complexities of LLM deployment and provides a structured approach for enhancing decision-making and operational efficiency with AI. Starting with an introduction to the foundational concepts, the book swiftly moves on to hands-on applications focusing on real-world challenges and solutions. You’ll master data strategies and explore design patterns that streamline the optimization and deployment of LLMs in enterprise environments. From fine-tuning techniques to advanced inferencing patterns, the book equips you with a toolkit for solving complex challenges and driving AI-led innovation in business processes. By the end of this book, you’ll have a solid grasp of key LLM design patterns and how to apply them to enhance the performance and scalability of your generative AI solutions.

What you will learn

Apply design patterns to integrate LLMs into enterprise applications for efficiency and scalability  Overcome common challenges in scaling and deploying LLMs  Use fine-tuning techniques and RAG approaches to enhance LLM efficiency Stay ahead of the curve with insights into emerging trends and advancements, including multimodality Optimize LLM performance through customized contextual models, advanced inferencing engines, and evaluation patterns Ensure fairness, transparency, and accountability in AI applications

Who this book is forThis book is designed for a diverse group of professionals looking to understand and implement advanced design patterns for LLMs in their enterprise applications, including AI and ML researchers exploring practical applications of LLMs, data scientists and ML engineers designing and implementing large-scale GenAI solutions, enterprise architects and technical leaders who oversee the integration of AI technologies into business processes, and software developers creating scalable GenAI-powered applications.

By: Ahmed Menshawy, Mahmoud Fahmy
Imprint: Packt Publishing Limited
Country of Publication: United Kingdom
Dimensions: Height: 235mm, Width: 191mm,
ISBN: 9781836203070
ISBN 10: 1836203071
Pages: 564
Publication Date: 19 September 2025
Audience: Professional and scholarly , Undergraduate
Format: Paperback
Publisher's Status: Active

Ahmed Menshawy is the Vice President of AI Engineering at Mastercard. He leads the AI Engineering team to drive the development and operationalization of AI products, address a broad range of challenges and technical debts for ML pipelines deployment. He also leads a team dedicated to creating several AI accelerators and capabilities, including serving engines and feature stores, aimed at enhancing various aspects of AI engineering. Mahmoud Fahmy is a Lead Machine Learning Engineer at Mastercard, specializing in the development and operationalization of AI products. His primary focus is on optimizing machine learning pipelines and navigating the intricate challenges of deploying models effectively for end customers.