Description: Introducing MLOpsHow to Scale Machine Learning in the Enterprise Author(s): Mark Treveil, Nicolas Omont, Clement Stenac, Kenji Lefevre, Du Phan, Joachim Zentici, Adrien Lavoillotte, Makoto Miyazaki, Lynn Heidmann Format: Paperback Publisher: O'Reilly Media, United States Imprint: O'Reilly Media ISBN-13: 9781492083290, 978-1492083290 Synopsis 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
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Book Title: Introducing MLOps
Item Height: 233 mm
Item Width: 178 mm
Author: Makoto Miyazaki, Clement Stenac, Lynn Heidmann, Adrien Lavoillotte, Nicolas Omont, Joachim Zentici, Mark Treveil, Kenji Lefevre, Du Phan
Publication Name: Introducing Mlops: How to Scale Machine Learning in the Enterprise
Format: Paperback
Language: English
Publisher: O'reilly Media, INC International Concepts USA
Subject: Computer Science
Publication Year: 2020
Type: Textbook
Number of Pages: 150 Pages
