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

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.

了解如何將自動機器學習應用於數據流，並創建無摩擦（需要最少或不需要人工干預）和彈性（可以通過控制數據流、算法和洞察的用戶數量來擴展或縮小）的交易機器學習（TML）解決方案。本書將加強您對使用與Apache Kafka集成的數據流的自動機器學習的TML解決方案的內部運作的了解。

《使用數據流和自動機器學習的交易機器學習》介紹了將機器學習應用於數據流時的行業挑戰。您將學習一個框架，幫助您選擇最適合TML的業務問題。您還將了解如何衡量TML解決方案的商業價值。然後，您將學習TML解決方案的技術組件，包括TML解決方案的參考和技術架構。

本書還提供了一個TML解決方案模板，讓您可以輕鬆快速地開始構建自己的TML解決方案。具體而言，您可以下載TML Python庫和集成技術。您還將了解TML在未來的演變以及組織對數據流更深入洞察的日益需求。

通過閱讀本書，您將對TML有扎實的理解。您將知道如何構建具有所有必要細節的TML解決方案，並且所有資源都近在咫尺。

您將學到什麼：

- 了解交易機器學習
- 衡量TML的商業價值
- 選擇TML的使用案例
- 設計使用Apache Kafka構建TML解決方案的技術架構
- 使用用於構建TML解決方案的技術
- 在雲端中使用Apache Kafka進行實際代碼操作構建交易機器學習解決方案

本書適合的讀者：

數據科學家、機器學習工程師和架構師，以及人工智能和機器學習的業務領導者。

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.

Sebastian Maurice是OTICS Advanced Analytics Inc.的創始人兼首席技術官，擁有超過25年的人工智慧和機器學習經驗。在此之前，Sebastian曾擔任Gartner Consulting的副董事，專注於人工智慧和機器學習。他在發展和擴大Gartner的人工智慧顧問業務方面發揮了重要作用。他帶領全球團隊利用機器學習解決了石油和天然氣、零售、公用事業、製造、金融和保險等領域的關鍵業務問題。Maurice博士在石油和天然氣（上游）方面也具有豐富的經驗，是加拿大首批將機器學習應用於油田生產優化的人之一，並獲得了加拿大專利：#2864265。

Sebastian也是一位發表過七篇國際同行評審期刊和書籍的作者。他的一篇論文（《國際工程教育期刊》，2004年）被引用為在線測試技術領域的里程碑性工作。他還開發了世界上第一個用於交易式機器學習的Apache Kafka連接器：MAADS-VIPER。

Maurice博士在卡爾加里大學獲得了電氣和計算機工程博士學位，並擁有電氣工程碩士學位和農業經濟學碩士學位，以及純數學學士學位和經濟學學士學位（榮譽學位）。

Maurice博士還在多倫多大學教授數據科學課程，並積極協助開發多倫多大學的人工智慧課程內容。他還活躍於人工智慧社區，是一位熱心的博客作者和演講者。他還擔任麥克馬斯特大學人工智慧顧問委員會的成員。