Transactional Machine Learning with Data Streams and AutoML:Build Frictionless and Elastic Machine Learning Solutions with Apache Kafka in the Cloud Using Python


Transactional Machine Learning with Data Streams and AutoML:Build Frictionless and Elastic Machine Learning Solutions with Apache Kafka in the Cloud Using Python
by:Sebastian Maurice
Publisher Finelybook 出版社:Apress; 1st ed. edition (20 May 2021)
Language 语言:English
pages 页数:292 pages
ISBN-10 书号:1484270223
ISBN-13 书号:9781484270226

Book Description
From the Back Cover
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.
You will:

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
About the Author
Sebastian Maurice is founder and CTO of OTICS Advanced Analytics Inc. and has over 25 years of experience in AI and machine learning. Previously, Sebastian served as Associate Director within Gartner Consulting focusing on artificial intelligence and machine learning. He was instrumental in developing and growing Gartner’s AI consulting business. He has led global teams to solve critical business problems with machine learning in oil and gas, retail, utilities, manufacturing, finance, and insurance. Dr. Maurice also brings deep experience in oil and gas (upstream) and was one of the first in Canada to apply machine learning to oil production optimization, which resulted in a Canadian patent:#2864265.

Sebastian is also a published author with seven publications in international peer-reviewed journals and books. One of his publications (International Journal of Engineering Education, 2004) was cited as landmark work in the area of online testing technology. He also developed the world’s first Apache Kafka connector for transactional machine learning:MAADS-VIPER.

Dr. Maurice received his PhD in electrical and computer engineering from the University of Calgary, and has a master’s in electrical engineering, and a master’s in agricultural economics, with bachelors in pure mathematics and bachelors (hon) in economics.
Dr. Maurice also teaches a course on data science at the University of Toronto and actively helps to develop AI course content at the University of Toronto. He is also active in the AI community and an avid blogger and speaker. He also sits on the AI advisory board at McMaster University.

t matter
无关紧要

1. Introduction:Big Data, Auto Machine Le
1.简介:大数据,汽车机械乐

2. Transactional machine
2.事务机器

4. The Business value of Transactional Machine Learning
4.事务性机器学习的商业价值

5. The Technical Components and Architecture for Transactional Machine Le
5.事务机的技术组件和体系结构

onal Machine Learning Solt
本机学习解决方案

emplate with Streaming visualiza
使用流可视化模板

Visualize Your TML Model Insights:Optimization, Predictions, and Anomalies
可视化您的TML模型见解:优化、预测和异常

ities for tr
tr的城市

tional Machine Learning in Almost Every Industry
几乎每个行业都有国家机器学习

TML Project Planning Approach and Clo
TML项目规划方法与Clo

下载地址:应版权方要求,该资源内容链接已移除!

你可以 登录 后获取帮助.

赞(0) 觉得文章有用就打赏一下
未经允许不得转载:finelybook » Transactional Machine Learning with Data Streams and AutoML:Build Frictionless and Elastic Machine Learning Solutions with Apache Kafka in the Cloud Using Python

评论 抢沙发

评论前必须登录!

 

觉得文章有用就打赏一下

支付宝扫一扫打赏

微信扫一扫打赏