| 書目名稱 | Transactional Machine Learning with Data Streams and AutoML | | 副標(biāo)題 | Build Frictionless a | | 編輯 | Sebastian Maurice | | 視頻video | http://file.papertrans.cn/928/927738/927738.mp4 | | 概述 | Explains transactional machine learning within a technical and business context that broadens the audience to technical developers, to mid managers, to business executives.Shows how automated machine | | 圖書封面 |  | | 描述 | .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 | | 出版日期 | Book 2021 | | 關(guān)鍵詞 | Machine Learning; Auto ML; Transactional Learning; Data Streams; Real Time Analytics; Python; Kafka; Algori | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4842-7023-3 | | isbn_softcover | 978-1-4842-7022-6 | | isbn_ebook | 978-1-4842-7023-3 | | copyright | Sebastian Maurice 2021 |
The information of publication is updating
|
|
|Archiver|手機(jī)版|小黑屋|
派博傳思國際
( 京公網(wǎng)安備110108008328)
GMT+8, 2026-1-28 05:55
發(fā)表于 2025-3-21 17:23:53
