| 書目名稱 | MLOps Lifecycle Toolkit | | 副標題 | A Software Engineeri | | 編輯 | Dayne Sorvisto | | 視頻video | http://file.papertrans.cn/621/620187/620187.mp4 | | 概述 | Explains deploying machine learning models with‘accuracy, extensibility, scalability, and reliability.Covers deploying ML systems in a variety of industries with case studies.Explains how to create va | | 圖書封面 |  | | 描述 | .This book is aimed at practitioners of data science, with consideration for bespoke problems, standards, and tech stacks between industries. It will guide you through the fundamentals of technical decision making, including planning, building, optimizing, packaging, and deploying end-to-end, reliable, and robust stochastic workflows using the language of data science...MLOps Lifecycle Toolkit.?walks you through the principles of software engineering, assuming no prior experience. It addresses the perennial “why” of MLOps early, along with insight into the unique challenges of engineering stochastic systems. Next, you’ll discover resources to learn software craftsmanship, data-driven testing frameworks, and computer science. Additionally, you will see how to transition from Jupyter notebooks to code editors, and leverage infrastructure and cloud services to take control of the entire machine learning lifecycle. You’ll gain insight into the technical and architectural decisions you’re likely to encounter, as well as best practices for deploying accurate, extensible, scalable, and reliable models. Through hands-on labs, you will build your own MLOps “toolkit” that you can use to acce | | 出版日期 | Book 2023 | | 關鍵詞 | Data Science; Machine Learning; DevOps; MLOps; Python; Business Intelligence | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4842-9642-4 | | isbn_softcover | 978-1-4842-9641-7 | | isbn_ebook | 978-1-4842-9642-4 | | copyright | Dayne Sorvisto 2023 |
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