Introducing MLOps: How to Scale Machine Learning in the Enterprise


Introducing MLOps: How to Scale Machine Learning in the Enterprise
By 作者:Mark Treveil (Author), Nicolas Omont (Author), Clément Stenac (Author), Kenji Lefevre (Author), Du Phan (Author), Joachim Zentici (Author), Adrien Lavoillotte (Author), Makoto Miyazaki (Author), Lynn Heidmann (Author)
Publisher Finelybook 出版社 : O'Reilly Media; 1st edition (December 22, 2020)
Language 语言: : English
pages 页数: 186 pages
ISBN-10 : 1492083291
ISBN-13 : 9781492083290
The Book Description robot was collected from Amazon and arranged by Finelybook
More than half of the analytics and machine learning (ML) models created By 作者: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 作者: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

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