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Full Description
Integrate large language models to transform your Enterprise Applications with Advanced LLM Strategies.
Purchase of the print or Kindle book includes a free PDF eBook.
Key Features
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.
Book DescriptionThe 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.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
Who this book is forThis 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.
Contents
Table of Contents
Introduction to Large Language Models (LLMs)
LLMs in Enterprise: Applications, Challenges, and Design Patterns
Data and Training in Foundation Models
Fine-Tuning and Retrieval-Augmented Generation (RAG) Patterns
Customizing Contextual LLMs Patterns
Evaluation Patterns
Enterprise Challenges in Evaluating LLM Applications
Data Strategy for LLMs
Model Deployment
Accelerated and Optimized Inferencing Patterns
LLMs in Production
RAG 2.0: Beyond Mainstream RAG
Connected LLMs Pattern
Responsible AI in LLMs
