| 书目名称 | MLOps with Ray | | 副标题 | Best Practices and S | | 编辑 | Hien Luu,Max Pumperla,Zhe Zhang | | 视频video | | | 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 | | 图书封面 |  | | 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 | | 关键词 | Python; Ray AIR; ML infrastructure; Machine Learning orchestration; Machine Learning; MLOps; Feature Engin | | 版次 | 1 | | doi | https://doi.org/10.1007/979-8-8688-0376-5 | | isbn_softcover | 979-8-8688-0375-8 | | isbn_ebook | 979-8-8688-0376-5 | | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to APress Media, LLC, part |
The information of publication is updating
|
|
发表于 2025-3-21 17:44:45
