Titlebook: MLOps with Ray; Best Practices and S Hien Luu,Max Pumperla,Zhe Zhang Book 2024 The Editor(s) (if applicable) and The Author(s), under exclu

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PRE

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An Introduction to the Ray AI Libraries,or offline batch inference. We will also explain when and why you should use Ray’s AI libraries and provide a brief overview of the Ray AI ecosystem. Lastly, we will delve into the connection between Ray and other systems.

出生

ations.Explains Ray open source project and how it might fit.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 learn

危機(jī)

MLOps Adoption Strategies and Case Studies,ext 12 months. The “AI Adoption in the Enterprise 2020” report from O’reily confirms such statistics and further shared that AI/ML adoption is pervasive across many industries, such as financial services, education, healthcare, manufacturing, retail, and more.

Pigeon

Feature Engineering Infrastructure,be used. First, the high-level details and benefits will be described. Next, the high-level architecture and its subcomponents will be discussed, and finally a few case studies, including home-grown, open source, and commercial vendor solutions, will be highlighted.

效果

Model Serving Infrastructure,ucture plays a crucial role in operationalizing ML models in production and integrating the ML projects into the operations of an organization, such as predicting customer churn, detecting fraudulent activities, personalizing customer experience, improving the quality of products and services, and more.

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