Practical Solutions for Modern Nlp Challenges: Mastering Llms and Slms for Real-World Nlp in Cloud and Open-Source
暫譯: 現代自然語言處理挑戰的實用解決方案：掌握LLMs和SLMs於雲端及開源環境中的實際應用

Gunnu, Venkata, Shah, Shubham, Minukuri, Anvesh

Practical Solutions for Modern Nlp Challenges: Mastering Llms and Slms for Real-World Nlp in Cloud and Open-Source
  • 出版商: Apress
  • 出版日期: 2026-01-03
  • 售價: $1,890
  • 貴賓價: 9.5 折 $1,796
  • 語言: 英文
  • 頁數: 539
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 9798868820557
  • ISBN-13: 9798868820557
  • 相關分類: Large language model

    • 海外代購書籍(需單獨結帳)

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商品描述

Large Language Models (LLMs) have revolutionized Natural Language Processing (NLP), enabling advanced applications such as machine translation, text summarization, and sentiment analysis. This book serves as a comprehensive guide for data scientists, machine learning engineers, and developers, offering foundational theory and practical skills to harness the power of LLMs for real-world problems. From understanding the fundamentals of LLMs to deploying them in cloud and open-source environments, this book equips readers with the essential knowledge to excel in modern NLP.

The book takes a hands-on approach, guiding readers through the end-to-end deployment of LLMs--from data collection and preprocessing to model training, evaluation, and real-time inference. Using popular frameworks like Amazon SageMaker and Hugging Face Transformers, you'll explore practical tasks such as text generation, classification, and named entity recognition. Additionally, it delves into industry use cases like customer support chatbots and content generation while addressing emerging trends, scaling techniques, and ethical considerations like bias and fairness in AI. This is your ultimate resource for mastering LLMs in production-ready environments.

You Will:

  • Learn to implement cutting-edge NLP tasks such as text generation, sentiment analysis, and named entity recognition using AWS services and open-source tools like Hugging Face.
  • Understand best practices for scaling and maintaining NLP models in production, focusing on real-time performance, monitoring, and iterative improvements.
  • Practice techniques for training and optimizing LLMs, covering data preprocessing, hyperparameter tuning, and evaluation strategies.

This book is for:

Data scientists, Machine learning engineers, and developers

商品描述(中文翻譯)

大型語言模型（LLMs）已經徹底改變了自然語言處理（NLP），使得機器翻譯、文本摘要和情感分析等先進應用成為可能。本書作為數據科學家、機器學習工程師和開發人員的全面指南，提供了基礎理論和實用技能，以利用LLMs的力量解決現實世界中的問題。從理解LLMs的基本原理到在雲端和開源環境中部署它們，本書為讀者提供了在現代NLP中脫穎而出的必要知識。

本書採取實作導向的方法，指導讀者完成LLMs的端到端部署——從數據收集和預處理到模型訓練、評估和實時推斷。使用像Amazon SageMaker和Hugging Face Transformers等流行框架，您將探索文本生成、分類和命名實體識別等實用任務。此外，本書還深入探討了行業用例，如客戶支持聊天機器人和內容生成，同時討論新興趨勢、擴展技術以及在人工智慧中的偏見和公平性等倫理考量。這是您在生產環境中掌握LLMs的終極資源。

您將：
- 學習如何使用AWS服務和開源工具（如Hugging Face）實現尖端的NLP任務，如文本生成、情感分析和命名實體識別。
- 理解在生產環境中擴展和維護NLP模型的最佳實踐，重點關注實時性能、監控和迭代改進。
- 實踐訓練和優化LLMs的技術，涵蓋數據預處理、超參數調整和評估策略。

本書適合：
數據科學家、機器學習工程師和開發人員

作者簡介

Anvesh currently serves as a VP, Sr Lead ML engineer (LLM) at JP Morgan Chase, specializing in NLP applications. With a fervent advocacy for data science and artificial intelligence, he boasts 11+ years in IT and 9 years of experience in the Analytics field executed predictive and prescriptive solutions. Holding a master's degree from Oklahoma State University, he majored in data mining, following his bachelor's in computer science from JNTU University in India. Originating from South India, he commenced his career as a Software Engineer, catering to esteemed Fortune 500 clients such as GE, Cisco, and Tech Mahindra. Additionally, he aided stakeholders in capitalizing on the true value of AI & ML using actionable data insights and was responsible for overseeing the design of ML.

Venkat Gunnu is a Senior Executive Director of Knowledge Management and Innovation at JPM Chase. He is an executive with a successful background crafting enterprise-wide data and data science solutions, GenAI, process improvements, and data and data science-centric products.

Shubham is a Software Engineer with expertise in machine learning, cloud technologies, and AI-powered solutions. I have experience developing and optimizing systems like Retrieval-Augmented Generation (RAG) models, integrating AI technologies like ChatGPT and Mistral for smarter, real-time information retrieval.

Jayanth is a seasoned Machine Learning Engineer with 12 years of experience, specializing in Python programming, large language models (LLM), ModelOps, and automation technologies. With a strong background in deploying and optimizing machine learning models, he excels in creating efficient workflows that streamline the model lifecycle from development to production.

Sundar Krishnan is seasoned Data Science leader with over 12 years of experience. As a Senior Manager at CVS Health, he oversees Data Science and Data Engineering teams, driving healthcare products to enhance member health outcomes.

作者簡介(中文翻譯)

Anvesh 目前擔任 JP Morgan Chase 的副總裁、高級首席機器學習工程師（LLM），專注於自然語言處理（NLP）應用。他熱衷於數據科學和人工智慧，擁有超過 11 年的 IT 經驗以及 9 年的分析領域經驗，執行預測和處方解決方案。他持有俄克拉荷馬州立大學的碩士學位，主修數據挖掘，並在印度 JNTU 大學獲得計算機科學學士學位。來自南印度的他，最初以軟體工程師的身份開始職業生涯，為 GE、Cisco 和 Tech Mahindra 等知名的 Fortune 500 客戶提供服務。此外，他協助利益相關者利用可行的數據洞察來發揮 AI 和機器學習的真正價值，並負責監督機器學習的設計。

Venkat Gunnu 是 JPM Chase 知識管理與創新部的高級執行董事。他是一位具有成功背景的高管，專注於制定企業範圍內的數據和數據科學解決方案、生成式人工智慧（GenAI）、流程改進以及以數據和數據科學為中心的產品。

Shubham 是一名擁有機器學習、雲技術和 AI 驅動解決方案專業知識的軟體工程師。我有開發和優化系統的經驗，例如檢索增強生成（RAG）模型，並整合 ChatGPT 和 Mistral 等 AI 技術，以實現更智能的即時信息檢索。

Jayanth 是一位經驗豐富的機器學習工程師，擁有 12 年的經驗，專注於 Python 編程、大型語言模型（LLM）、模型運營（ModelOps）和自動化技術。他在部署和優化機器學習模型方面具有堅實的背景，擅長創建高效的工作流程，簡化從開發到生產的模型生命周期。

Sundar Krishnan 是一位經驗豐富的數據科學領導者，擁有超過 12 年的經驗。作為 CVS Health 的高級經理，他負責數據科學和數據工程團隊，推動醫療產品以改善會員的健康結果。

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