Essential Guide to LLMOps: Implementing effective LLMOps strategies and tools from data to deployment
Doan, Ryan
- 出版商: Packt Publishing
- 出版日期: 2024-07-31
- 售價: $1,820
- 貴賓價: 9.5 折 $1,729
- 語言: 英文
- 頁數: 190
- 裝訂: Quality Paper - also called trade paper
- ISBN: 1835887503
- ISBN-13: 9781835887509
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相關分類:
LangChain
海外代購書籍(需單獨結帳)
商品描述
Unlock the secrets to mastering LLMOps with innovative approaches to streamline AI workflows, improve model efficiency, and ensure robust scalability, revolutionizing your language model operations from start to finish
Key Features:
- Gain a comprehensive understanding of LLMOps, from data handling to model governance
- Leverage tools for efficient LLM lifecycle management, from development to maintenance
- Discover real-world examples of industry cutting-edge trends in generative AI operation
- Purchase of the print or Kindle book includes a free PDF eBook
Book Description:
The rapid advancements in large language models (LLMs) bring significant challenges in deployment, maintenance, and scalability. This Essential Guide to LLMOps provides practical solutions and strategies to overcome these challenges, ensuring seamless integration and the optimization of LLMs in real-world applications.
This book takes you through the historical background, core concepts, and essential tools for data analysis, model development, deployment, maintenance, and governance. You'll learn how to streamline workflows, enhance efficiency in LLMOps processes, employ LLMOps tools for precise model fine-tuning, and address the critical aspects of model review and governance. You'll also get to grips with the practices and performance considerations that are necessary for the responsible development and deployment of LLMs. The book equips you with insights into model inference, scalability, and continuous improvement, and shows you how to implement these in real-world applications.
By the end of this book, you'll have learned the nuances of LLMOps, including effective deployment strategies, scalability solutions, and continuous improvement techniques, equipping you to stay ahead in the dynamic world of AI.
What You Will Learn:
- Understand the evolution and impact of LLMs in AI
- Differentiate between LLMOps and traditional MLOps
- Utilize LLMOps tools for data analysis, preparation, and fine-tuning
- Master strategies for model development, deployment, and improvement
- Implement techniques for model inference, serving, and scalability
- Integrate human-in-the-loop strategies for refining LLM outputs
- Grasp the forefront of emerging technologies and practices in LLMOps
Who this book is for:
This book is for machine learning professionals, data scientists, ML engineers, and AI leaders interested in LLMOps. It is particularly valuable for those developing, deploying, and managing LLMs, as well as academics and students looking to deepen their understanding of the latest AI and machine learning trends. Professionals in tech companies and research institutions, as well as anyone with foundational knowledge of machine learning will find this resource invaluable for advancing their skills in LLMOps.
Table of Contents
- Introduction to LLMs and LLMOps
- Reviewing LLMOps Components
- Processing Data in LLMOps Tools
- Developing Models via LLMOps
- LLMOps Review and Compliance
- LLMOps Strategies for Inference, Serving, and Scalability
- LLMOps Monitoring and Continuous Improvement
- The Future of LLMOps and Emerging Technologies
商品描述(中文翻譯)
解鎖掌握 LLMOps 的秘密，透過創新的方法簡化 AI 工作流程、提升模型效率，並確保穩健的可擴展性，徹底改變您的語言模型操作。
主要特點：
- 從數據處理到模型治理，全面了解 LLMOps
- 利用工具高效管理 LLM 生命週期，從開發到維護
- 發現行業前沿趨勢在生成 AI 操作中的實際案例
- 購買印刷版或 Kindle 書籍可獲得免費 PDF 電子書
書籍描述：
大型語言模型（LLMs）的快速進展帶來了在部署、維護和可擴展性方面的重大挑戰。本書《LLMOps 必備指南》提供了實用的解決方案和策略，以克服這些挑戰，確保 LLM 在實際應用中的無縫整合和優化。
本書將帶您了解歷史背景、核心概念以及數據分析、模型開發、部署、維護和治理的基本工具。您將學會如何簡化工作流程、提升 LLMOps 流程的效率、使用 LLMOps 工具進行精確的模型微調，並處理模型審查和治理的關鍵方面。您還將掌握負責任地開發和部署 LLM 所需的實踐和性能考量。本書提供了有關模型推理、可擴展性和持續改進的見解，並展示如何在實際應用中實施這些內容。
在本書結束時，您將學會 LLMOps 的細微差別，包括有效的部署策略、可擴展性解決方案和持續改進技術，使您能在動態的 AI 世界中保持領先。
您將學到的內容：
- 了解 LLM 在 AI 中的演變和影響
- 區分 LLMOps 和傳統 MLOps
- 利用 LLMOps 工具進行數據分析、準備和微調
- 精通模型開發、部署和改進的策略
- 實施模型推理、服務和可擴展性的技術
- 整合人機協作策略以精煉 LLM 輸出
- 掌握 LLMOps 中新興技術和實踐的前沿
本書適合對 LLMOps 感興趣的機器學習專業人士、數據科學家、ML 工程師和 AI 領導者。對於那些開發、部署和管理 LLM 的人來說，這本書特別有價值，同時也適合希望深入了解最新 AI 和機器學習趨勢的學者和學生。科技公司和研究機構的專業人士，以及任何具備機器學習基礎知識的人，都會發現這本資源對提升他們在 LLMOps 的技能非常重要。
目錄：
- LLM 和 LLMOps 簡介
- 審查 LLMOps 組件
- 在 LLMOps 工具中處理數據
- 通過 LLMOps 開發模型
- LLMOps 審查和合規
- LLMOps 的推理、服務和可擴展性策略
- LLMOps 監控和持續改進
- LLMOps 的未來和新興技術
