MLOps with Ray: best practices and strategies for adopting machine learning operations
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 compet...
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| Beteiligte Personen: | , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Berkeley, CA
Apress L. P.
2024
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| Schlagwörter: | |
| Links: | https://learning.oreilly.com/library/view/-/9798868803765/?ar |
| Zusammenfassung: | 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 Who This Book Is For Machine learning practitioners, data scientists, and software engineers who are focusing on building machine learning systems and infrastructure to bring ML models to production. |
| Beschreibung: | Description based upon print version of record. - Chapter 4: Model Training Infrastructure |
| Umfang: | 1 Online-Ressource (xi, 338 Seiten) illustrations. |
| ISBN: | 9798868803765 |
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| indexdate | 2025-01-17T11:22:11Z |
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| isbn | 9798868803765 |
| language | English |
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| spelling | Luu, Hien VerfasserIn aut MLOps with Ray best practices and strategies for adopting machine learning operations Hien Luu, Max Pumperla, Zhe Zhang Berkeley, CA Apress L. P. 2024 1 Online-Ressource (xi, 338 Seiten) illustrations. Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Description based upon print version of record. - Chapter 4: Model Training Infrastructure 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 Who This Book Is For Machine learning practitioners, data scientists, and software engineers who are focusing on building machine learning systems and infrastructure to bring ML models to production. Machine learning Computer software Development Apprentissage automatique Pumperla, Max VerfasserIn aut Zhang, Zhe VerfasserIn aut 9798868803758 Erscheint auch als Druck-Ausgabe 9798868803758 |
| spellingShingle | Luu, Hien Pumperla, Max Zhang, Zhe MLOps with Ray best practices and strategies for adopting machine learning operations Machine learning Computer software Development Apprentissage automatique |
| title | MLOps with Ray best practices and strategies for adopting machine learning operations |
| title_auth | MLOps with Ray best practices and strategies for adopting machine learning operations |
| title_exact_search | MLOps with Ray best practices and strategies for adopting machine learning operations |
| title_full | MLOps with Ray best practices and strategies for adopting machine learning operations Hien Luu, Max Pumperla, Zhe Zhang |
| title_fullStr | MLOps with Ray best practices and strategies for adopting machine learning operations Hien Luu, Max Pumperla, Zhe Zhang |
| title_full_unstemmed | MLOps with Ray best practices and strategies for adopting machine learning operations Hien Luu, Max Pumperla, Zhe Zhang |
| title_short | MLOps with Ray |
| title_sort | mlops with ray best practices and strategies for adopting machine learning operations |
| title_sub | best practices and strategies for adopting machine learning operations |
| topic | Machine learning Computer software Development Apprentissage automatique |
| topic_facet | Machine learning Computer software Development Apprentissage automatique |
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