Theory and Practice of Quality Assurance for Machine Learning Systems: An Experiment-Driven Approach
暫譯: 機器學習系統品質保證的理論與實踐：以實驗為驅動的方法
Ackerman, Samuel, Barash, Guy, Farchi, Eitan
- 出版商: Springer
- 出版日期: 2024-10-27
- 售價: $2,300
- 貴賓價: 9.5 折 $2,185
- 語言: 英文
- 頁數: 182
- 裝訂: Quality Paper - also called trade paper
- ISBN: 3031700074
- ISBN-13: 9783031700071
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相關分類:
Machine Learning
海外代購書籍(需單獨結帳)
相關主題
商品描述
This book is a self-contained introduction to engineering and testing machine learning (ML) systems. It systematically discusses and teaches the art of crafting and developing software systems that include and surround machine learning models. Crafting ML based systems that are business-grade is highly challenging, as it requires statistical control throughout the complete system development life cycle. To this end, the book introduces an "experiment first" approach, stressing the need to define statistical experiments from the beginning of the development life cycle and presenting methods for careful quantification of business requirements and identification of key factors that impact business requirements. Applying these methods reduces the risk of failure of an ML development project and of the resultant, deployed ML system. The presentation is complemented by numerous best practices, case studies and practical as well as theoretical exercises and their solutions, designed to facilitate understanding of the ideas, concepts and methods introduced.
The goal of this book is to empower scientists, engineers, and software developers with the knowledge and skills necessary to create robust and reliable ML software.
商品描述(中文翻譯)
這本書是對工程和測試機器學習（ML）系統的自成一體的介紹。它系統性地討論並教授設計和開發包含及圍繞機器學習模型的軟體系統的藝術。打造商業級的基於機器學習的系統是非常具有挑戰性的，因為這需要在整個系統開發生命週期中進行統計控制。為此，本書引入了一種「實驗優先」的方法，強調在開發生命週期的開始就需要定義統計實驗，並提出小心量化商業需求和識別影響商業需求的關鍵因素的方法。應用這些方法可以降低機器學習開發專案及其部署後的機器學習系統失敗的風險。這些內容還輔以眾多最佳實踐、案例研究以及實踐和理論練習及其解答，旨在促進對所介紹的思想、概念和方法的理解。
本書的目標是使科學家、工程師和軟體開發人員具備創建穩健且可靠的機器學習軟體所需的知識和技能。
作者簡介
Samuel Ackerman earned his Ph.D. in statistics from Temple University in Philadelphia, PA, in 2018. Since then, he has worked as a statistician and data science researcher at IBM Research Israel in Haifa, actively contributing to the development of machine learning (ML) testing and analysis methods and tools.
Guy Barash earned his M.Sc. in computer science with a focus on AI, from Bar Ilan University in 2021. His scientific research examines vulnerabilities of ML software. For eight years, he has been working in the software industry - both corporate and startup - on the design and implementation of reliable ML-based systems.
Eitan Farchi earned his Ph.D. in game theory from Haifa University in Israel, in 2000. He is a distinguished engineer at IBM Research and works on the development of methods, tools and field solutions for quality and reliability of software systems. Recently, he focused on quality and reliability of industrial strength ML-based solutions in the area of intelligent chatbot software.
Orna Raz holds a Ph.D. in Software Engineering from Carnegie Mellon University. Over the years, she has studied the quality of industrial strength software. Recently, she focused on ML-based systems and has conceptualized and developed FreaAI - a slice-based ML software analysis tool that is used for industrial ML software quality analysis.
Onn Shehory is a professor of Intelligent Information Systems at Bar Ilan University (BIU), Israel, where he also serves as the director of the Data Science and AI Institute. He has many years of both academic and industrial experience in the fields of AI and software engineering. In recent years his research focused on ML, its vulnerabilities, and methods for mitigating related risks.
作者簡介(中文翻譯)
塞繆爾·阿克曼於2018年在賓夕法尼亞州費城的天普大學獲得統計學博士學位。自那時以來，他一直在以色列海法的IBM研究所擔任統計學家和數據科學研究員，積極貢獻於機器學習（ML）測試和分析方法及工具的開發。
蓋·巴拉什於2021年在巴伊蘭大學獲得計算機科學碩士學位，專注於人工智慧（AI）。他的科學研究檢視機器學習軟體的漏洞。在過去的八年中，他在軟體產業（包括企業和初創公司）工作，專注於可靠的基於機器學習的系統的設計和實施。
艾坦·法爾希於2000年在以色列海法大學獲得博弈論博士學位。他是IBM研究所的傑出工程師，專注於軟體系統的質量和可靠性的方法、工具及現場解決方案的開發。最近，他專注於工業強度的基於機器學習的解決方案在智能聊天機器人軟體領域的質量和可靠性。
奧娜·拉茲擁有卡內基梅隆大學的軟體工程博士學位。多年來，她研究工業強度軟體的質量。最近，她專注於基於機器學習的系統，並構思和開發了FreaAI——一種基於切片的機器學習軟體分析工具，用於工業機器學習軟體的質量分析。
昂·謝霍里是以色列巴伊蘭大學（BIU）智能資訊系統的教授，並擔任數據科學與人工智慧研究所的主任。他在人工智慧和軟體工程領域擁有多年學術和產業經驗。近年來，他的研究專注於機器學習、其漏洞及減輕相關風險的方法。
