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A Comprehensive Guide to Leverage Generative AI in the Modern EnterpriseKey Features? Gain a comprehensive understanding of LLMs within the framework of Generative AI, from foundational concepts to advanced applications.? Dive into practical exercises and real-world applications, accompanied by detailed code walkthroughs in Python.? Explore LLMOps with a dedicated focus on ensuring trustworthy AI and best practices for deploying, managing, and maintaining LLMs in enterprise settings.Book Description“Mastering Large Language Models with Python” is an indispensable resource that offers a comprehensive exploration of Large Language Models (LLMs), providing the essential knowledge to leverage these transformative AI models effectively. From unraveling the intricacies of LLM architecture to practical applications like code generation and AI-driven recommendation systems, readers will gain valuable insights into implementing LLMs in diverse projects. Covering both open-source and proprietary LLMs, the book delves into foundational concepts and advanced techniques, empowering professionals to harness the full potential of these models. Detailed discussions on quantization techniques for efficient deployment, operational strategies with LLMOps, and ethical considerations ensure a well-rounded understanding of LLM implementation.Through real-world case studies, code snippets, and practical examples, readers will navigate the complexities of LLMs with confidence, paving the way for innovative solutions and organizational growth. Whether you seek to deepen your understanding, drive impactful applications, or lead AI-driven initiatives, this book equips you with the tools and insights needed to excel in the dynamic landscape of artificial intelligence.What you will learn? In-depth study of LLM architecture and its versatile applications across industries.? Harness open-source and proprietary LLMs to craft innovative solutions.? Implement LLM APIs for a wide range of tasks spanning natural language processing, audio analysis, and visual recognition.? Optimize LLM deployment through techniques such as quantization and operational strategies like LLMOps, ensuring efficient and scalable model usage.Table of Contents1. The Basics of Large Language Models and Their Applications2. Demystifying Open-Source Large Language Models3. Closed-Source Large Language Models4. LLM APIs for Various Large Language Model Tasks5. Integrating Cohere API in Google Sheets6. Dynamic Movie Recommendation Engine Using LLMs7. Document-and Web-based QA Bots with Large Language Models8. LLM Quantization Techniques and Implementation9. Fine-tuning and Evaluation of LLMs10. Recipes for Fine-Tuning and Evaluating LLMs11. LLMOps - Operationalizing LLMs at Scale12. Implementing LLMOps in Practice Using MLflow on Databricks13. Mastering the Art of Prompt Engineering14. Prompt Engineering Essentials and Design Patterns15. Ethical Considerations and Regulatory Frameworks for LLMs16. Towards Trustworthy Generative AI (A Novel Framework Inspired by Symbolic Reasoning) IndexAbout the AuthorRaj Arun is a distinguished expert in Artificial Intelligence and Data Science, with over fifteen years of industry experience. He holds an MBA from the Indian Institute of Management, Tiruchirapalli, and specialized diplomas in blockchain technology from the Indian Institute of Technology in Madras and Guwahati. Raj is also certified as a quantum Qiskit developer from IBM, marking him as a versatile professional who bridges the gap between traditional computing and quantum advancements.