商品描述
Explore emerging technologies and the evolving role of AI in finance. Geared toward finance professionals, this book will equip you with the knowledge and tools to harness the power of Large Language Models (LLMs), ensuring you stay ahead in an increasingly AI-driven industry. Highlighting the benefits and challenges of LLMs in financial contexts, the book starts with the necessary infrastructure setup, covering both hardware and software requirements. It offers a balanced discussion on cloud versus on-premises solutions, enabling you to make informed decisions based on their specific needs. Training and fine-tuning LLMs are critical components of effective deployment, and this book offers best practices, from data preparation to advanced fine-tuning techniques. It also delves into deployment strategies, with practical advice on building deployment pipelines, monitoring performance, and optimizing operations. Ensuring data privacy and security is paramount in finance, so you'll take a close look at maintaining compliance with regulations while safeguarding sensitive information. You'll also examine the integration of LLMs into existing financial systems, with real-world case studies and strategies for API development and real-time data processing. Monitoring and maintenance are crucial for long-term success, and the book outlines how to manage performance metrics, handle model drift, and ensure regular updates. Large Language Models Ops for Finance is your essential guide to discovering the transformative potential of LLMs in the finance industry. What You Will Learn ● Review LLMs and their applications in finance. ● Set up the infrastructure for training and deploying LLMs. ● Apply best practices for fine-tuning and maintaining LLMs. ● Employ techniques for integrating LLMs into existing financial systems Who This Book Is For AI and ML engineers, data scientists, and finance professionals interested in implementing and managing large language models within the finance industry.
商品描述(中文翻譯)
探索新興技術及人工智慧在金融領域中不斷演變的角色。本書針對金融專業人士,將為您提供利用大型語言模型(Large Language Models, LLMs)力量所需的知識和工具,確保您在日益以人工智慧驅動的行業中保持領先。
本書強調LLMs在金融環境中的優勢與挑戰,首先介紹必要的基礎設施設置,涵蓋硬體和軟體需求。它對雲端解決方案與本地解決方案進行了平衡的討論,使您能根據具體需求做出明智的決策。訓練和微調LLMs是有效部署的關鍵組成部分,本書提供了從數據準備到高級微調技術的最佳實踐。它還深入探討了部署策略,提供有關建立部署管道、監控性能和優化操作的實用建議。
在金融領域,確保數據隱私和安全至關重要,因此您將仔細研究在保護敏感信息的同時維持合規性。您還將檢視LLMs如何整合到現有金融系統中,並提供真實案例研究及API開發和即時數據處理的策略。監控和維護對於長期成功至關重要,本書概述了如何管理性能指標、處理模型漂移以及確保定期更新。《大型語言模型運營指南:金融篇》是您發現LLMs在金融行業中轉型潛力的必備指南。
您將學到的內容:
● 回顧LLMs及其在金融中的應用。
● 設置訓練和部署LLMs的基礎設施。
● 應用微調和維護LLMs的最佳實踐。
● 採用技術將LLMs整合到現有金融系統中。
本書適合對象:
對於希望在金融行業內實施和管理大型語言模型的人工智慧和機器學習工程師、數據科學家及金融專業人士。
作者簡介
Brindha Priyadarshini Jeyaraman is a seasoned AI and Data Science leader with over 16 years of experience spanning machine learning, software engineering, and cloud architecture. As Principal Architect for AI, APAC at Google Cloud, she leads AI-driven transformations across diverse sectors including telecommunications, finance, gaming, and AI agents for the consumer ecosystem. An expert in real-time systems, MLOps, and Temporal Knowledge Graphs, Brindha has authored three influential books on machine learning, streaming analytics, and financial observability. Her technical leadership has significantly advanced AI adoption and deployment practices across the APAC region, strengthening partner ecosystems and shaping enterprise AI strategies. She holds a Master's in Knowledge Engineering from the National University of Singapore and a Doctor of Engineering in AI with a specialization in Temporal Knowledge Graphs in Finance from Singapore Management University. Brindha is deeply passionate about mentoring the next generation of AI professionals and championing diversity and inclusion in the tech industry. Brindha is recognized for her innovative approach to solving complex problems and is a leading voice in the AI community.
作者簡介(中文翻譯)
Brindha Priyadarshini Jeyaraman 是一位資深的人工智慧 (AI) 和數據科學領導者,擁有超過 16 年的經驗,涵蓋機器學習、軟體工程和雲端架構。作為 Google Cloud 亞太區的 AI 首席架構師,她領導著各行各業的 AI 驅動轉型,包括電信、金融、遊戲以及消費者生態系統中的 AI 代理。Brindha 是即時系統、MLOps 和時間知識圖譜的專家,並著有三本有影響力的書籍,主題涵蓋機器學習、串流分析和金融可觀察性。她的技術領導力顯著推進了亞太地區的 AI 採用和部署實踐,強化了夥伴生態系統並塑造了企業 AI 策略。
她擁有新加坡國立大學的知識工程碩士學位,以及新加坡管理大學的人工智慧博士學位,專攻金融領域的時間知識圖譜。Brindha 對於指導下一代 AI 專業人才充滿熱情,並積極倡導科技產業中的多樣性和包容性。Brindha 以其創新的問題解決方法而聞名,是 AI 社群中的領軍人物。