MLOps Engineering with Python : Production Machine Learning Pipelines, Deployment, Monitoring, and Infrastructure
Language: English
Published by Independently Published Mai 2026, 2026
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 56.60
Quantity: 2 available
Add to basketItem description from seller
Neuware - Reactive PublishingMLOps Engineering with Python is a practical guide to building, deploying, and maintaining machine learning systems in production environments.This book introduces the core engineering practices behind modern machine learning operations, including pipeline design, model packaging, deployment workflows, monitoring, version control, automation, and infrastructure planning. Rather than treating machine learning as a one-time modeling exercise, it focuses on the full operational lifecycle required to move models from experimentation into reliable production use.Readers will learn how Python-based tools and workflows can support reproducible machine learning pipelines, structured deployment processes, model performance tracking, and scalable system design. The book also examines the engineering tradeoffs involved in managing data, features, models, environments, and production feedback loops.Designed for machine learning engineers, data scientists, software developers, and technical teams working with production AI systems, this guide provides a structured foundation for understanding how MLOps connects machine learning development with real-world operational reliability.…
Seller Inventory # 9798197709158
- Title
- MLOps Engineering with Python : Production Machine Learning Pipelines, Deployment, Monitoring, and Infrastructure
- Author
- Takehiro Kanegi
- Publisher
- Independently Published Mai 2026
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 13
- 9798197709158
- Item weight
- 655 grams
- Dimensions
- 229x152x35 mm
MLOps Engineering with Python is a practical guide to building, deploying, and maintaining machine learning systems in production environments.
This book introduces the core engineering practices behind modern machine learning operations, including pipeline design, model packaging, deployment workflows, monitoring, version control, automation, and infrastructure planning. Rather than treating machine learning as a one-time modeling exercise, it focuses on the full operational lifecycle required to move models from experimentation into reliable production use.
Readers will learn how Python-based tools and workflows can support reproducible machine learning pipelines, structured deployment processes, model performance tracking, and scalable system design. The book also examines the engineering tradeoffs involved in managing data, features, models, environments, and production feedback loops.
Designed for machine learning engineers, data scientists, software developers, and technical teams working with production AI systems, this guide provides a structured foundation for understanding how MLOps connects machine learning development with real-world operational reliability.
"Synopsis" may belong to another edition of this title.
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