In the past six months, many industry friends have inquired about the progress of the publication of "Introduction and Practice of Machine Learning Testing" through various channels. Thank you very much for your enthusiasm and expectations for this book. After continuous polishing and improvement, I can finally tell you aloud this time.
"Introduction and Practice of Machine Learning Testing" finally meet you!
"Introduction and Practice of Machine Learning Testing" is being pre-sold on the JD platform.
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writing background
With the development of science and technology, artificial intelligence has gradually penetrated into various fields of society, such as smart cities, smart finance, and smart homes. Artificial intelligence technology is changing our lives in all directions at an unprecedented speed and leading a new round of industrial transformation. In order to seize the strategic opportunities for the development of artificial intelligence, many companies are actively doing digital transformation and upgrading. While the field of artificial intelligence is facing major development opportunities, it is also facing huge challenges. This puts forward higher requirements for the technical level and professional knowledge of every practitioner and person with lofty ideals in the field of artificial intelligence.
Machine learning is one of the most important directions in the field of artificial intelligence. With the increasing popularity of machine learning applications and the complexity of its own technology, the quality of machine learning applications has become more and more prominent. This is mainly reflected in the aspects of data quality, feature engineering, model effects, and product functions. For example, the quality of training data will lead to the unreliability of machine learning models, which may eventually lead to wrong conclusions and make wrong decisions (predictions). While bringing quality risks, it will also bring costs up. According to IBM's 2016 study on data costs, the annual economic cost loss is about 3.1 trillion US dollars due to poor data quality alone. It can be seen that ensuring the quality of machine learning applications is an important part of its application in business.
For the testing of traditional software and Internet products, the testing methods and quality assurance system have been relatively mature. And machine learning testing is a different and newer direction. We can't make a mechanical copy of traditional software and Internet product testing methods, and there are few complete machine learning quality systems in the industry to learn from. Faced with the technical challenges of machine learning testing, in the past two years, the author has formulated and completed a series of technical tackling actions. Start from three aspects: first through the training of machine learning professional courses, systematically learning machine learning techniques, and proficient in the process of modeling training; then based on special technical practices (big data automation, automatic feature analysis, model effect evaluation, model Experiment management, etc.), continue to accumulate practical experience in machine learning testing, and gradually build a quality system for machine learning applications; finally, combine business scenarios to make further supplements and improvements (model deployment, model monitoring, etc.).
The original intention of writing this book is to share the author's practice and experience accumulation in machine learning testing with industry friends, jointly promote the development of machine learning testing, and help industry friends to win future opportunities in the "new infrastructure" wave.
Executive summary
This book comprehensively and systematically introduces machine learning testing technology and quality system construction, a total of 15 chapters, divided into 5 parts.
- Part 1 (Chapters 1-4) Basic Knowledge covers the basic knowledge of machine learning, Python programming, and data analysis;
- Part 2 (Chapters 5-7) Big Data introduces the basics of big data, big data testing guidelines and related tool practice;
- Part 3 (Chapters 8-10) Model Testing explains the basics of machine learning testing, feature special testing, and model algorithm evaluation testing;
- Part 4 (Chapters 11-13) Model Engineering introduces the practice of model evaluation platform, machine learning engineering technology and machine learning continuous delivery process;
- Part 5 (Chapters 14-15) AI in Test discusses the exploration and practice of AI in the testing field and the future of test engineers in the AI era.
This book can help readers understand how machine learning works and how to get started with the quality assurance of machine learning. This book is suitable for general readers who want to understand machine learning and is suitable for technical workers who want to learn and master machine learning.
For engineering development and testing engineers, by reading this book, you can systematically understand the knowledge of big data testing, feature testing and model evaluation; for engineers with algorithmic data background, you can learn and broaden the methods and means of model evaluation by reading this book. The idea of model engineering practice; for technical experts and technical managers, by reading this book, you can gain ideas for machine learning quality assurance and engineering efficiency.
Book features
The first book , the industry's first AI test cheats.
Easy to understand , select 15 AI test points, start from scratch, and fully understand machine learning tests.
The content is rich , covering 5 major technical topics, big data, model algorithms, model evaluation, engineering architecture, and intelligent testing.
Highly recommended , 32 test experts, algorithm experts, technical leaders, and university professors jointly recommended.
Typical scenarios , detailed explanation of 3 mainstream model scenarios, image classification, advertisement recommendation, and financial risk control. In-depth analysis of the technical principles and engineering framework of the credit risk control model, and output a full-link quality assurance plan for the risk control model products.
Collecting the best of hundreds of schools , it has absorbed the characteristics of excellent works on the market, such as using more illustrations in the process of explaining the theory; the code adopts a color style, which is more intuitive to read. More than 150 examples of illustrations, explanations with illustrations. Full text is printed in color for the best experience.
About the Author
Experts recommend
As the industry's first book to systematically explain AI testing, this book has received wide attention and unanimous praise from industry experts.
Here is an excerpt of some wonderful reviews, the content is as follows:
This book mainly focuses on the field of machine learning. Machine learning is the core of artificial intelligence algorithms. The contents of the book are all the practice and experience accumulation of the 360 AI test team in machine learning testing. It is hoped that through this kind of effective sharing and learning, technical exchanges in the field of artificial intelligence can be better promoted, and ideas can burst into more sparks in collisions. ——Ye Daqing, co-founder and CEO of Rong360
Different from traditional textbooks related to machine learning theories, this book has a novel perspective, expounds the quality assurance and engineering effectiveness of machine learning products from a QA perspective, and focuses on how machine learning testing methods can be implemented in real business scenarios. The book is rich in content, and the chapter design is step by step (machine learning, data quality, model evaluation, model engineering, intelligent testing), the content is easy to understand, and a large number of real cases are listed (such as: financial risk control, intelligent recommendation, image classification, etc.) , I hope to help readers learn and understand. —— Liu Caofeng, co-founder and CTO of Rong360
I have heard of it, and I hope that more engineers who are interested in exquisite machine learning technology and perfect machine learning methods can read this book earlier, get a systematic summary from it, understand the author’s experience, and feel that there is a specialization in the art industry. It can be applied to daily work and become a technical expert in the field. ——Jiang Fan, Senior Scientist of Alibaba Local Life
Today I am very happy to have such a book that tells us how to test intelligent systems. For example, feature specific testing and model algorithm evaluation testing. This aspect of testing is also the core of artificial intelligence testing and the key content of this book. It discusses model transformation testing, fuzzing testing, robustness testing, interpretability testing, etc., including image classification, intelligent recommendation, financial Model algorithm evaluation practices in three typical business scenarios, such as risk control, will do a more detailed analysis and interpretation of this part of the content, so that readers can better understand the model algorithm evaluation method, process and specific operations. —— Zhu Shaomin, Distinguished Professor of Tongji University and author of "Whole Software Testing"
Different from the model test in the traditional modeling process, this book explains how to pass the quality test in the various stages of data acquisition, modeling test, model launch, and online AB test in the development of machine learning products from the perspective of the work of testers. Ensure the correct development and launch of machine learning models. The quality test of machine learning not only covers traditional test angles such as system stability, functional correctness, throughput, etc., but also needs to cover data correctness, model accuracy, model offline calculation and online calculation consistency, etc. Algorithm engineers usually care about The problem. This book brings a wealth of first-hand practical experience and novel perspectives in the research and development of machine learning products, and has good enlightenment for practitioners who are committed to the research and development of artificial intelligence products. - AI Ctrip large data and application development, Head, VP Zou Yu
and traditional machine learning compared to books, the book is more focused on how to test the application of machine learning and AI intelligent product testing. This book is rich in content and detailed cases. It not only explains the basic knowledge of data analysis, machine learning, big data, etc., but also explains the principles and case studies of model algorithm evaluation, feature test analysis, model engineering platform, etc. This book is easy to understand and practical. It is very suitable for students who want to get started with machine learning and big data testing. ——Shen Jian, CTO of Kuaigou Taxi, author of the public account "Road to Architect"
The development of artificial intelligence is in full swing, and machine learning is the most important thing. Machines that can learn to play Go, play games, and autopilot are no problem. Testing is a necessary part of the engineering quality system. How to test machine learning when no intelligent machine has passed the Turing test? How to measure the IQ of artificial intelligence? Do you need higher artificial intelligence? Can machine learning be used to test the effect of machine learning? This kind of problem seems to be more interesting than machine learning itself, and the author of this book will tell us about it. ——Shi Haifeng, 2B2C CTO of Shell Financial Services, author of the public account "IT Migrant Workers Gossip"
Testing in the field of machine learning is a relatively new direction, and the testing system is still in the stage of gradual improvement. As a senior AI testing team leader, Ai Hui systematically introduced the testing technology in the field of machine learning in this book, and made a detailed development of machine learning models and engineering testing, indicating for AI testing engineers The learning route. After reading this book, you will definitely have a more comprehensive understanding of AI testing work, which will greatly help expand your horizons and actual combat. —— Xu Mingquan, Head of SF City's Artificial Intelligence Center
With the development and popularization of big data and artificial intelligence technologies, machine learning-based solutions are playing an increasingly important role in the modeling and optimization of business problems, and related quality assurance of models, features, and effects is also more important . Compared with the quality assurance of engineering architecture and product functions, machine learning models are more difficult to guarantee due to their nonlinearity and sensitivity. The author summarizes his long-term project experience in related fields and presents it in this book. It is expected to have certain inspiration and reference significance for related students. ——Yan Kuiming, Didi Senior Algorithm Expert
The Internet has entered the second half, and the protagonist of the second half is artificial intelligence. As the most important field of artificial intelligence, machine learning is a fairly new field, and testing based on machine learning is a path few people have traveled. There are not many companies that do machine learning, and the team that focuses on machine learning testing is Fengmao Lingjiao. Ai Hui and the Rong360AI testing team led by him, the practice and first-line practical experience in machine learning testing are very valuable at the moment. Up. Today, when artificial intelligence is exploding, the market has waited too long for a book dedicated to testing machine learning technology. All test colleagues who are eager to embrace artificial intelligence should put this book on the shelves of Rong360 AI. A valuable book of first-line testing experience for the testing team. It is recommended that you finish the book as soon as possible. When you are actually doing artificial intelligence related tests, you can use it from time to time to look at the experience of Ai Hui and his team. I believe it will be of great help. —— Xu Kun, President of Testin Cloud Testing
Ten years ago, when I was working in Ali, I started testing machine learning models. At that time, the test data for algorithm models was very scarce. Ten years have passed. The entire Internet has been running on the basis of big data processing, machine learning, deep learning, and artificial intelligence. The entire society has been deeply integrated with these technologies. How to ensure the quality and evaluate the effect is already an important work direction for social development. However, due to various reasons, the corresponding test system has not been out of the scientific research circle. The book of Ai Hui’s team filled this gap in time and promoted the development of algorithm model testing, machine learning and AI testing. It is expected that more AI will emerge in the testing industry in the future. Summary of testing practices. ——Huang Yansheng (Si Han), founder of Hogwarts Test Academy and test architect of Test Bar (Beijing) Technology Co., Ltd.
The future has come, and the time we live in is completely different from the past. Artificial intelligence and big data technology have been transformed from the altar of top scientific research technology to practical technology. With the lowering of the threshold for the use of technology, the field of software testing and software quality has also found an opportunity to combine with them, and a new intelligence has been developed. The branch of software testing technology has been implemented and practiced in the industry. This book systematically explains the exploration and practice of the Rong360 team in this frontier field of testing. It adopts a knowledge structure system design from shallow to deep, combined with easy-to-understand language, to help readers get rid of the cocoon and master the key technologies. It is a book A good book on the introduction of intelligent testing technology and engineering practice.
——Dell EMC (China Research and Development Group) senior architect, Tencent Cloud's most valuable expert TVP, Alibaba Cloud's most valuable expert MVP Ru Bingsheng
For more wonderful comments from experts, please see the preface and praise part of this book.
Pre-sale description: The first day of pre-sale (8.28), a surprise price of 50% off, only this day, the hand is slow.