Transactional Machine Learning with Data Streams and AutoML

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

Who This Book Is For 

Data scientists, machine learning engineers and architects,  and AI and machine learning business leaders.

Publication Year: 2021
Imprint: Apress