Articles liés à A Guide to Implementing MLOps: From Data to Operations
Synopsis
Over the past decade, machine learning has come a long way, with organisations of all sizes exploring its potential to extract valuable insights from data. However, despite the promise of machine learning, many organisations need help deploying and managing machine learning models in production. This is where MLOps comes in. MLOps, or machine learning operations, is an emerging field that focuses on the deployment, management, and monitoring of machine learning models in production environments. MLOps combines the principles of DevOps with the unique requirements of machine learning, enabling organisations to build and deploy models at scale while maintaining high levels of reliability and accuracy. This book is a comprehensive guide to MLOps, providing readers with a deep understanding of the principles, best practices, and emerging trends in the field. From training models to deploying them in production, the book covers all aspects of the MLOps process, providing readers with the knowledge and tools they need to implement MLOps in their organisations. The book is aimed at data scientists, machine learning engineers, and IT professionals who are interested in deploying machine learning models at scale. It assumes a basic understanding of machine learning concepts and programming, but no prior knowledge of MLOps is required. Whether you're just getting started with MLOps or looking to enhance your existing knowledge, this book is an essential resource for anyone interested in scaling machine learning in production.
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À propos de l?auteur
Prafful Mishra is a seasoned engineer with extensive experience in operationalizing machine learning across organizations of varying scales. His expertise includes Site Reliability & Platform Engineering, and artificial intelligence, with a particular focus on MLOps. Prafful is passionate about emerging technologies such as quantum computing, federated learning, and explainable AI. He actively shares his insights through writing and speaking engagements, aiming to demystify complex concepts and foster innovation in the tech community. A strong advocate for open-source contributions, Prafful supports the democratization of technology, believing that collaborative development leads to more accessible and robust solutions.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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A Guide to Implementing MLOps
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A Guide to Implementing MLOps: From Data to Operations (Synthesis Lectures on Engineering, Science, and Technology)
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A Guide to Implementing MLOps : From Data to Operations
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Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Over the past decade, machine learning has come a long way, with organisations of all sizes exploring its potential to extract valuable insights from data. However, despite the promise of machine learning, many organisations need help deploying and managing machine learning models in production. This is where MLOps comes in. MLOps, or machine learning operations, is an emerging field that focuses on the deployment, management, and monitoring of machine learning models in production environments. MLOps combines the principles of DevOps with the unique requirements of machine learning, enabling organisations to build and deploy models at scale while maintaining high levels of reliability and accuracy. This book is a comprehensive guide to MLOps, providing readers with a deep understanding of the principles, best practices, and emerging trends in the field. From training models to deploying them in production, the book covers all aspects of the MLOps process, providing readers with the knowledge and tools they need to implement MLOps in their organisations. The book is aimed at data scientists, machine learning engineers, and IT professionals who are interested in deploying machine learning models at scale. It assumes a basic understanding of machine learning concepts and programming, but no prior knowledge of MLOps is required. Whether you're just getting started with MLOps or looking to enhance your existing knowledge, this book is an essential resource for anyone interested in scaling machine learning in production. N° de réf. du vendeur 9783031820090
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A Guide to Implementing MLOps
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Springer, Berlin, Springer Nature Switzerland, Springer, 2025
ISBN 10 : 3031820096
ISBN 13 : 9783031820090
Neuf
Couverture rigide
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Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Over the past decade, machine learning has come a long way, with organisations of all sizes exploring its potential to extract valuable insights from data. However, despite the promise of machine learning, many organisations need help deploying and managing machine learning models in production. This is where MLOps comes in. MLOps, or machine learning operations, is an emerging field that focuses on the deployment, management, and monitoring of machine learning models in production environments. MLOps combines the principles of DevOps with the unique requirements of machine learning, enabling organisations to build and deploy models at scale while maintaining high levels of reliability and accuracy. This book is a comprehensive guide to MLOps, providing readers with a deep understanding of the principles, best practices, and emerging trends in the field. From training models to deploying them in production, the book covers all aspects of the MLOps process, providing readers with the knowledge and tools they need to implement MLOps in their organisations. The book is aimed at data scientists, machine learning engineers, and IT professionals who are interested in deploying machine learning models at scale. It assumes a basic understanding of machine learning concepts and programming, but no prior knowledge of MLOps is required. Whether you're just getting started with MLOps or looking to enhance your existing knowledge, this book is an essential resource for anyone interested in scaling machine learning in production. 132 pp. Englisch. N° de réf. du vendeur 9783031820090
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A Guide to Implementing MLOps: From Data to Operations (Synthesis Lectures on Engineering, Science, and Technology)
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A Guide to Implementing MLOps
ISBN 10 : 3031820096
ISBN 13 : 9783031820090
Neuf
Couverture rigide
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
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Buch. Etat : Neu. Neuware -Over the past decade, machine learning has come a long way, with organisations of all sizes exploring its potential to extract valuable insights from data. However, despite the promise of machine learning, many organisations need help deploying and managing machine learning models in production. This is where MLOps comes in. MLOps, or machine learning operations, is an emerging field that focuses on the deployment, management, and monitoring of machine learning models in production environments. MLOps combines the principles of DevOps with the unique requirements of machine learning, enabling organisations to build and deploy models at scale while maintaining high levels of reliability and accuracy. This book is a comprehensive guide to MLOps, providing readers with a deep understanding of the principles, best practices, and emerging trends in the field. From training models to deploying them in production, the book covers all aspects of the MLOps process, providing readers with the knowledge and tools they need to implement MLOps in their organisations. The book is aimed at data scientists, machine learning engineers, and IT professionals who are interested in deploying machine learning models at scale. It assumes a basic understanding of machine learning concepts and programming, but no prior knowledge of MLOps is required. Whether you're just getting started with MLOps or looking to enhance your existing knowledge, this book is an essential resource for anyone interested in scaling machine learning in production.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 148 pp. Englisch. N° de réf. du vendeur 9783031820090
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A Guide to Implementing Mlops: From Data to Operations
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Springer-Nature New York Inc, 2025
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A Guide to Implementing MLOps (Hardcover)
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Springer International Publishing AG, Cham, 2025
ISBN 10 : 3031820096
ISBN 13 : 9783031820090
Neuf
Couverture rigide
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Hardcover. Etat : new. Hardcover. Over the past decade, machine learning has come a long way, with organisations of all sizes exploring its potential to extract valuable insights from data. However, despite the promise of machine learning, many organisations need help deploying and managing machine learning models in production. This is where MLOps comes in. MLOps, or machine learning operations, is an emerging field that focuses on the deployment, management, and monitoring of machine learning models in production environments. MLOps combines the principles of DevOps with the unique requirements of machine learning, enabling organisations to build and deploy models at scale while maintaining high levels of reliability and accuracy. This book is a comprehensive guide to MLOps, providing readers with a deep understanding of the principles, best practices, and emerging trends in the field. From training models to deploying them in production, the book covers all aspects of the MLOps process, providing readers with the knowledge and tools they need to implement MLOps in their organisations. The book is aimed at data scientists, machine learning engineers, and IT professionals who are interested in deploying machine learning models at scale. It assumes a basic understanding of machine learning concepts and programming, but no prior knowledge of MLOps is required. Whether you're just getting started with MLOps or looking to enhance your existing knowledge, this book is an essential resource for anyone interested in scaling machine learning in production. Over the past decade, machine learning has come a long way, with organisations of all sizes exploring its potential to extract valuable insights from data. MLOps, or machine learning operations, is an emerging field that focuses on the deployment, management, and monitoring of machine learning models in production environments. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9783031820090
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A Guide to Implementing MLOps (Hardcover)
Edité par
Springer International Publishing AG, Cham, 2025
ISBN 10 : 3031820096
ISBN 13 : 9783031820090
Neuf
Couverture rigide
Vendeur : AussieBookSeller, Truganina, VIC, Australie
Évaluation du vendeur 5 sur 5 étoiles
Hardcover. Etat : new. Hardcover. Over the past decade, machine learning has come a long way, with organisations of all sizes exploring its potential to extract valuable insights from data. However, despite the promise of machine learning, many organisations need help deploying and managing machine learning models in production. This is where MLOps comes in. MLOps, or machine learning operations, is an emerging field that focuses on the deployment, management, and monitoring of machine learning models in production environments. MLOps combines the principles of DevOps with the unique requirements of machine learning, enabling organisations to build and deploy models at scale while maintaining high levels of reliability and accuracy. This book is a comprehensive guide to MLOps, providing readers with a deep understanding of the principles, best practices, and emerging trends in the field. From training models to deploying them in production, the book covers all aspects of the MLOps process, providing readers with the knowledge and tools they need to implement MLOps in their organisations. The book is aimed at data scientists, machine learning engineers, and IT professionals who are interested in deploying machine learning models at scale. It assumes a basic understanding of machine learning concepts and programming, but no prior knowledge of MLOps is required. Whether you're just getting started with MLOps or looking to enhance your existing knowledge, this book is an essential resource for anyone interested in scaling machine learning in production. Over the past decade, machine learning has come a long way, with organisations of all sizes exploring its potential to extract valuable insights from data. MLOps, or machine learning operations, is an emerging field that focuses on the deployment, management, and monitoring of machine learning models in production environments. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. N° de réf. du vendeur 9783031820090
Quantité disponible : 1 disponible(s)
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A Guide to Implementing MLOps (Hardcover)
Edité par
Springer International Publishing AG, Cham, 2025
ISBN 10 : 3031820096
ISBN 13 : 9783031820090
Neuf
Couverture rigide
Vendeur : Grand Eagle Retail, Mason, OH, Etats-Unis
Évaluation du vendeur 5 sur 5 étoiles
Hardcover. Etat : new. Hardcover. Over the past decade, machine learning has come a long way, with organisations of all sizes exploring its potential to extract valuable insights from data. However, despite the promise of machine learning, many organisations need help deploying and managing machine learning models in production. This is where MLOps comes in. MLOps, or machine learning operations, is an emerging field that focuses on the deployment, management, and monitoring of machine learning models in production environments. MLOps combines the principles of DevOps with the unique requirements of machine learning, enabling organisations to build and deploy models at scale while maintaining high levels of reliability and accuracy. This book is a comprehensive guide to MLOps, providing readers with a deep understanding of the principles, best practices, and emerging trends in the field. From training models to deploying them in production, the book covers all aspects of the MLOps process, providing readers with the knowledge and tools they need to implement MLOps in their organisations. The book is aimed at data scientists, machine learning engineers, and IT professionals who are interested in deploying machine learning models at scale. It assumes a basic understanding of machine learning concepts and programming, but no prior knowledge of MLOps is required. Whether you're just getting started with MLOps or looking to enhance your existing knowledge, this book is an essential resource for anyone interested in scaling machine learning in production. Over the past decade, machine learning has come a long way, with organisations of all sizes exploring its potential to extract valuable insights from data. MLOps, or machine learning operations, is an emerging field that focuses on the deployment, management, and monitoring of machine learning models in production environments. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9783031820090
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