Transactional Machine Learning with Data Streams and AutoML
Год издания: 2021
Автор: Maurice S.
Издательство: Apress
ISBN: 978-1-4842-7023-3
Язык: Английский
Формат: PDF/ePub
Качество: Издательский макет или текст (eBook)
Интерактивное оглавление: Да
Количество страниц: 284
Описание: Understand how to apply auto machine learning to data streams and create transactional machine learning (TML) solutions that are frictionless (require minimal to no human intervention) and elastic (machine learning solutions that can scale up or down by controlling the number of data streams, algorithms, and users of the insights). This book will strengthen your knowledge of the inner workings of TML solutions using data streams with auto machine learning integrated with Apache Kafka.
Transactional Machine Learning with Data Streams and AutoML introduces the industry challenges with applying machine learning to data streams. You will learn the framework that will help you in choosing business problems that are best suited for TML. You will also see how to measure the business value of TML solutions. You will then learn the technical components of TML solutions, including the reference and technical architecture of a TML solution.
This book also presents a TML solution template that will make it easy for you to quickly start building your own TML solutions. Specifically, you are given access to a TML Python library and integration technologies for download. You will also learn how TML will evolve in the future, and the growing need by organizations for deeper insights from data streams. By the end of the book, you will have a solid understanding of TML. You will know how to build TML solutions with all the necessary details, and all the resources at your fingertips.
What You Will Learn
- Discover transactional machine learning
- Measure the business value of TML
- Choose TML use cases
- Design technical architecture of TML solutions with Apache Kafka
- Work with the technologies used to build TML solutions
- Build transactional machine learning solutions with hands-on code together with Apache Kafka in the cloud
Оглавление
About the Author ix
About the Technical Reviewer xi
Acknowledgments xiii
Introduction xv
Chapter 1: Introduction: Big Data, Auto Machine Learning, and Data Streams 1
Chapter 2: Transactional Machine Learning 25
Chapter 3: Overcoming Challenges to ML Adoption 61
Chapter 4: The Business Value of Transactional Machine Learning 77
Chapter 5: The Technical Components and Architecture for Transactional Machine Learning Solutions 109
Chapter 6: Transactional Machine Learning Solution Template with Streaming Visualization 137
Chapter 7: Visualize Your TML Model Insights: Optimization, Predictions, and Anomalies 207
Chapter 8: Evolution and Opportunities for Transactional Machine Learning in Almost Every Industry 223
Chapter 9: TML Project Planning Approach and Closing Thoughts 243
Definitions 259
References 263
Index 269
