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Introducing MLOps: How to Scale Machine Learning in the Enterprise

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Marque : GENERIC
Vendu par HEAVENBOOKS.MA

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 ke...

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Description produit

Marque
GENERIC
Titre
Introducing MLOps: How to Scale Machine Learning in the Enterprise
Éditeur
O'Reilly Media
Type de produit
paperback
Présentation du livre
paperback
Date de sortie
1/5/2021 12:00:00 AM
Langue d'origine
English
ISBN
1492083291
Nombre de pages
183 pages
Langue
English
Résumé
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 Read more
Auteur
Mark Treveil, Nicolas Omont, Clément Stenac, Kenji Lefevre, Du Phan, Joachim Zentici, Adrien Lavoillotte, Makoto Miyazaki, Lynn Heidmann
Date de parution
1/5/2021 12:00:00 AM