03673nam a22005415i 4500 979-8-8688-0376-5 DE-He213 20251030112316.0 cr nn 008mamaa 240617s2024 xxu| s |||| 0|eng d 9798868803765 979-8-8688-0376-5 10.1007/979-8-8688-0376-5 doi Q325.5-.7 UYQM bicssc MAT029000 bisacsh UYQM thema Luu, Hien. author. aut http://id.loc.gov/vocabulary/relators/aut 78812 MLOps with Ray [electronic resource] : Best Practices and Strategies for Adopting Machine Learning Operations / by Hien Luu, Max Pumperla, Zhe Zhang. 1st ed. 2024. Berkeley, CA : Apress : Imprint: Apress, 2024. XI, 338 p. 111 illus. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda 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 . Machine learning. 55059 Python (Computer program language). 64608 Artificial intelligence. 58441 Machine Learning. 55059 Python. 47553 Artificial Intelligence. 58441 Pumperla, Max. author. aut http://id.loc.gov/vocabulary/relators/aut 78813 Zhang, Zhe. author. aut http://id.loc.gov/vocabulary/relators/aut 78814 SpringerLink (Online service) Springer Nature eBook Printed edition: 9798868803758 Printed edition: 9798868803772 Printed edition: 9798868810862 https://doi.org/10.1007/979-8-8688-0376-5 ZDB-2-CWD ZDB-2-SXPC 60785 60785