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MLOps with Raybest practices and str...

Luu, Hien.

MLOps with Raybest practices and strategies for adopting machine learning operations /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	MLOps with Rayby Hien Luu, Max Pumperla, Zhe Zhang.
其他題名:	best practices and strategies for adopting machine learning operations /
作者:	Luu, Hien.
其他作者:	Pumperla, Max.
出版者:	Berkeley, CA :Apress :2024.
面頁冊數:	xi, 338 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Machine learning.
電子資源:	https://doi.org/10.1007/979-8-8688-0376-5
ISBN:	9798868803765$q(electronic bk.)

MLOps with Raybest practices and strategies for adopting machine learning operations /
Luu, Hien.

MLOps with Raybest practices and strategies for adopting machine learning operations /[electronic resource] :by Hien Luu, Max Pumperla, Zhe Zhang. - Berkeley, CA :Apress :2024. - xi, 338 p. :ill., digital ;24 cm.

Chapter 1: Introduction to MLOps -- Chapter 2: MLOps Adoption Strategy and Case Studies -- Chapter 3: Feature Engineering Infrastructure -- Chapter 4: Model Training Infrastructure -- Chapter 5: Model Serving -- Chapter 6: Machine Learning Observability -- Chapter 7: Ray Core -- Chapter 8: Ray Air -- Chapter 9: The Future of MLOps.

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 its components Get familiar with the MLOps adoption strategy Understand feature engineering.

ISBN: 9798868803765$q(electronic bk.)

Standard No.: 10.1007/979-8-8688-0376-5doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5 / .L88 2024

Dewey Class. No.: 006.31

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520 $a 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 its components Get familiar with the MLOps adoption strategy Understand feature engineering.
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