Implementing MLOps in the Enterprise
Language: English
Published by O'Reilly Media, US, 2023
- Softcover
- New
Seller: Rarewaves USA United, HEBRON, KY, U.S.A.Rarewaves USA United
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With demand for scaling, real-time access, and other capabilities, businesses need to consider building operational machine learning pipelines. This practical guide helps your company bring data science to life for different real-world MLOps scenarios. Senior data scientists, MLOps engineers, and machine learning engineers will learn how to tackle challenges that prevent many businesses from moving ML models to production.Authors Yaron Haviv and Noah Gift take a production-first approach. Rather than beginning with the ML model, you'll learn how to design a continuous operational pipeline, while making sure that various components and practices can map into it. By automating as many components as possible, and making the process fast and repeatable, your pipeline can scale to match your organization's needs.You'll learn how to provide rapid business value while answering dynamic MLOps requirements. This book will help you:Learn the MLOps process, including its technological and business valueBuild and structure effective MLOps pipelinesEfficiently scale MLOps across your organizationExplore common MLOps use casesBuild MLOps pipelines for hybrid deployments, real-time predictions, and composite AILearn how to prepare for and adapt to the future of MLOpsEffectively use pre-trained models like HuggingFace and OpenAI to complement your MLOps strategy.…
Seller Inventory # LU-9781098136581
- Title
- Implementing MLOps in the Enterprise
- Author
- Yaron Haviv, Noah Gift
- Publisher
- O'Reilly Media, US
- Publication year
- 2023
- Condition
- New
- Binding
- Paperback
- Language
- English
- ISBN 10
- 1098136586
- ISBN 13
- 9781098136581
With demand for scaling, real-time access, and other capabilities, businesses need to consider building operational machine learning pipelines. This practical guide helps your company bring data science to life for different real-world MLOps scenarios. Senior data scientists, MLOps engineers, and machine learning engineers will learn how to tackle challenges that prevent many businesses from moving ML models to production.
Authors Yaron Haviv and Noah Gift take a production-first approach. Rather than beginning with the ML model, you'll learn how to design a continuous operational pipeline, while making sure that various components and practices can map into it. By automating as many components as possible, and making the process fast and repeatable, your pipeline can scale to match your organization's needs.
You'll learn how to provide rapid business value while answering dynamic MLOps requirements. This book will help you:
- Learn the MLOps process, including its technological and business value
- Build and structure effective MLOps pipelines
- Efficiently scale MLOps across your organization
- Explore common MLOps use cases
- Build MLOps pipelines for hybrid deployments, real-time predictions, and composite AI
- Build production applications with LLMs and Generative AI, while reducing risks, increasing the efficiency, and fine tuning models
- Learn how to prepare for and adapt to the future of MLOps
- Effectively use pre-trained models like HuggingFace and OpenAI to complement your MLOps strategy
"Synopsis" may belong to another edition of this title.
About the Author
Noah Gift is the founder of Pragmatic A.I. Labs. Noah Gift lectures at MSDS, at Northwestern, Duke MIDS Graduate Data Science Program, the Graduate Data Science program at UC Berkeley, the UC Davis Graduate School of Management MSBA program, UNC Charlotte Data Science Initiative and University of Tennessee (as part of the Tennessee Digital Jobs Factory). He teaches and designs graduate machine learning, MLOps, A.I., Data Science courses, and consulting on Machine Learning and Cloud Architecture for students and faculty. These responsibilities include leading a multi-cloud certification initiative for students.
"About the title" may belong to another edition of this title.
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