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Titlebook: MLOps with Ray; Best Practices and S Hien Luu,Max Pumperla,Zhe Zhang Book 2024 The Editor(s) (if applicable) and The Author(s), under exclu

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發(fā)表于 2025-3-21 17:44:45 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱MLOps with Ray
副標(biāo)題Best Practices and S
編輯Hien Luu,Max Pumperla,Zhe Zhang
視頻videohttp://file.papertrans.cn/621/620188/620188.mp4
概述Covers up-to-date best practices and innovations in MLOps.Explains MLOps with case studies where it has been successfully adopted in organizations.Explains Ray open source project and how it might fit
圖書封面Titlebook: MLOps with Ray; Best Practices and S Hien Luu,Max Pumperla,Zhe Zhang Book 2024 The Editor(s) (if applicable) and The Author(s), under exclu
描述.Understand how to use MLOps as an engineering discipline to help with the challenges of bringing machine learning models to production quickly and consistently. This book will help companies worldwide to adopt and incorporate machine learning into their processes and products to improve their competitiveness...The book delves into this engineering discipline‘s aspects and components and explores best practices and case studies. Adopting MLOps requires a sound strategy, which the book‘s early chapters cover in detail. The book also discusses the infrastructure and best practices of Feature Engineering, Model Training, Model Serving, and Machine Learning Observability. Ray, the open source project that provides a unified framework and libraries to scale machine learning workload and the Python application, is introduced, and you will see how it fits into the MLOps technical stack...This book is intended for machine learning practitioners, such as machine learning engineers, and data scientists, who wish to help their company by adopting, building maps, and practicing MLOps...?..What You‘ll Learn.. .Gain an understanding of the MLOps discipline. .Know the MLOps technical stack and it
出版日期Book 2024
關(guān)鍵詞Python; Ray AIR; ML infrastructure; Machine Learning orchestration; Machine Learning; MLOps; Feature Engin
版次1
doihttps://doi.org/10.1007/979-8-8688-0376-5
isbn_softcover979-8-8688-0375-8
isbn_ebook979-8-8688-0376-5
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to APress Media, LLC, part
The information of publication is updating

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發(fā)表于 2025-3-21 23:21:37 | 只看該作者
板凳
發(fā)表于 2025-3-22 01:49:36 | 只看該作者
地板
發(fā)表于 2025-3-22 04:36:20 | 只看該作者
Hien Luu,Max Pumperla,Zhe Zhang sufficiently large number of components. The whole method becomes clear when the reader goes through the proofs and finds out that this method allows to solve several problems that had been considered hopeless for solving before. The reward for the efforts of the reader going along the lines of rat
5#
發(fā)表于 2025-3-22 08:47:27 | 只看該作者
Hien Luu,Max Pumperla,Zhe Zhang frequently cited in this book, especially in the Appendix, and we therefore mark them by short labels as [B], [N], [E], and [G]. We emphasize that there are also “Exercises” in [B], a “Problem Section” with contributions by several authors on pages 1063–1105 of [G], which are often of a combinatori
6#
發(fā)表于 2025-3-22 15:50:55 | 只看該作者
Hien Luu,Max Pumperla,Zhe Zhang sufficiently large number of components. The whole method becomes clear when the reader goes through the proofs and finds out that this method allows to solve several problems that had been considered hopeless for solving before. The reward for the efforts of the reader going along the lines of rat
7#
發(fā)表于 2025-3-22 19:39:11 | 只看該作者
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發(fā)表于 2025-3-22 23:51:44 | 只看該作者
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發(fā)表于 2025-3-23 04:49:37 | 只看該作者
Book 2024ook is intended for machine learning practitioners, such as machine learning engineers, and data scientists, who wish to help their company by adopting, building maps, and practicing MLOps...?..What You‘ll Learn.. .Gain an understanding of the MLOps discipline. .Know the MLOps technical stack and it
10#
發(fā)表于 2025-3-23 06:39:12 | 只看該作者
Introduction to MLOps,ge amount of data, and easily access computing power in the last decade has contributed to many advancements in the ML field, such as image recognition, language translation, and large language models (LLMs), that is, BERT, DALLE, ChatGPT, and more.
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