Introducing MLOps: How to Scale Machine Learning in the Enterprise
Language: English
Published by O'Reilly Media, 2020
- Softcover
- Used
Seller: World of Books Inc, Montgomery, IL, U.S.A.World of Books Inc
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Add to basketItem description from seller
This book introduces the key concepts of MLOps to help data scientists and application engineers not only operationalize ML models to drive real business change but also maintain and improve those models over time.
Seller Inventory # CIN1492083291G
- Title
- Introducing MLOps: How to Scale Machine Learning in the Enterprise
- Author
- Mark Treveil
- Publisher
- O'Reilly Media
- Publication year
- 2020
- Condition
- Good
- Binding
- Paperback
- Language
- English
- ISBN 10
- 1492083291
- ISBN 13
- 9781492083290
More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Some of the challenges and barriers to operationalization are technical, but others are organizational. Either way, the bottom line is that models not in production can't provide business impact.
This book introduces the key concepts of MLOps to help data scientists and application engineers not only operationalize ML models to drive real business change but also maintain and improve those models over time. Through lessons based on numerous MLOps applications around the world, nine experts in machine learning provide insights into the five steps of the model life cycle--Build, Preproduction, Deployment, Monitoring, and Governance--uncovering how robust MLOps processes can be infused throughout.
This book helps you:
- Fulfill data science value by reducing friction throughout ML pipelines and workflows
- Refine ML models through retraining, periodic tuning, and complete remodeling to ensure long-term accuracy
- Design the MLOps life cycle to minimize organizational risks with models that are unbiased, fair, and explainable
- Operationalize ML models for pipeline deployment and for external business systems that are more complex and less standardized
"Synopsis" may belong to another edition of this title.
About the Author
Nicolas Omont is VP of operations at Artelys where he is developing mathematical optimization solutions for energy and transport. He previously held the role of Dataiku Product Manager for ML and advanced analytics. He holds a PhD in Computer Science, and he's been working in operations research and statistics for the past 15 years, mainly in the telecommunications and energy utility sectors.
Clément Stenac is a passionate software engineer, CTO and co-founder at Dataiku. He oversees the design, development of the Dataiku DSS Entreprise AI Platform. Clément was previously head of product development at Exalead, leading the design and implementation of web-scale search engine software. He also has extensive experience with open source software, as a former developer of the VideoLAN (VLC) and Debian projects.
Kenji Lefevre is VP Product at Dataiku. He oversees the product roadmap and the user experience of the Dataiku DSS Entreprise AI Platform. He holds a PhD in pure mathematics from University of Paris VII, and he directed documentary movies before switching to Data Science and product management.
Du Phan is a Machine Learning engineer at Dataiku, where he works in democratizing data science. In the past few years, he has been dealing with a variety of data problems, from geospatial analysis to deep learning. His work now focuses on different facets and challenges of MLOps.
"About the title" may belong to another edition of this title.
World of Books Inc
Montgomery, IL, U.S.A.
AbeBooks seller since March 23, 2026
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