Introducing MLOps
Regular price
€65.99
A01=Adrien Lavoillotte
A01=Clement Stenac
A01=Du Phan
A01=Joachim Zentici
A01=Kenji Lefevre
A01=Lynn Heidmann
A01=Makoto Miyazaki
A01=Mark Treveil
A01=Nicolas Omont
Author_Adrien Lavoillotte
Author_Clement Stenac
Author_Du Phan
Author_Joachim Zentici
Author_Kenji Lefevre
Author_Lynn Heidmann
Author_Makoto Miyazaki
Author_Mark Treveil
Author_Nicolas Omont
Category=UYQM
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
MLOps Machine Learning Scaling Production enterprise ML environment deployment model governance CI/CD Dataiku
MLOps Machine Learning Scaling Production enterprise ML environment deployment model governance CICD Dataiku
Product details
- ISBN 9781492083290
- Dimensions: 178 x 233mm
- Publication Date: 31 Dec 2020
- Publisher: O'Reilly Media
- Publication City/Country: US
- Product Form: Paperback
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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
