Articles liés à Machine Learning in Production: From Models to Products

Machine Learning in Production: From Models to Products - Couverture rigide

Kastner, Christian

 
9780262049726: Machine Learning in Production: From Models to Products
Couverture rigide
ISBN 10 : 0262049724 ISBN 13 : 9780262049726
Editeur : The MIT Press, 2025

Synopsis

A practical and innovative textbook detailing how to build real-world software products with machine learning components, not just models.


Traditional machine learning texts focus on how to train and evaluate the machine learning model, while MLOps books focus on how to streamline model development and deployment. But neither focus on how to build actual products that deliver value to users. This practical textbook, by contrast, details how to responsibly build products with machine learning components, covering the entire development lifecycle from requirements and design to quality assurance and operations. Machine Learning in Production brings an engineering mindset to the challenge of building systems that are usable, reliable, scalable, and safe within the context of real-world conditions of uncertainty, incomplete information, and resource constraints. Based on the author’s popular class at Carnegie Mellon, this pioneering book integrates foundational knowledge in software engineering and machine learning to provide the holistic view needed to create not only prototype models but production-ready systems.

•   Integrates coverage of cutting-edge research, existing tools, and real-world applications
•   Provides students and professionals with an engineering view for production-ready machine learning systems
•   Proven in the classroom
•   Offers supplemental resources including slides, videos, exams, and further readings

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

À propos de l'auteur

Christian Kästner is associate professor of computer science at Carnegie Mellon University.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
The MIT Press
Date d'édition
2025
Langue
anglais
ISBN 10 
0262049724
ISBN 13 
9780262049726
Reliure
Relié
Nombre de pages
616
Coordonnées du fabricant
non disponible
Personne responsable
non disponible