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MLOps with Ray = best practices and strategies for adopting machine learning operations /

Record Type:	Electronic resources : Monograph/item
Title/Author:	MLOps with Ray/ by Hien Luu, Max Pumperla, Zhe Zhang.
Reminder of title:	best practices and strategies for adopting machine learning operations /
Author:	Luu, Hien.
other author:	Pumperla, Max.
Published:	Berkeley, CA :Apress : : 2024.,
Description:	xi, 338 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Machine learning. -
Online resource:	https://doi.org/10.1007/979-8-8688-0376-5
ISBN:	9798868803765

MLOps with Ray = best 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

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

533906
Machine learning.

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

Dewey Class. No.: 006.31

MLOps with Ray = best practices and strategies for adopting machine learning operations /
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505 0 $a 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.
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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