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Distributed Machine Learning Patterns

Af: Yuan Tang Engelsk Paperback

Distributed Machine Learning Patterns

Af: Yuan Tang Engelsk Paperback
Tjek vores konkurrenters priser
Practical patterns for scaling machine learning from your laptop to a distributed cluster.

In  Distributed Machine Learning Patterns you will learn how to:

  • Apply distributed systems patterns to build scalable and reliable machine learning projects
  • Construct machine learning pipelines with data ingestion, distributed training, model serving, and more
  • Automate machine learning tasks with Kubernetes, TensorFlow, Kubeflow, and Argo Workflows
  • Make trade offs between different patterns and approaches
  • Manage and monitor machine learning workloads at scale
Scaling up models from standalone devices to large distributed clusters is one of the biggest challenges faced by modern machine learning practitioners. Distributed Machine Learning Patterns teaches you how to scale machine learning models from your laptop to large distributed clusters. 

In Distributed Machine Learning Patterns, you''ll learn how to apply established distributed systems patterns to machine learning projects, and explore new ML-specific patterns as well. Firmly rooted in the real world, this book demonstrates how to apply patterns using examples based in TensorFlow, Kubernetes, Kubeflow, and Argo Workflows. Real-world scenarios, hands-on projects, and clear, practical DevOps techniques let you easily launch, manage, and monitor cloud-native distributed machine learning pipelines

Distributed Machine Learning Patterns teaches you how to scale machine learning models from your laptop to large distributed clusters. In it, you''ll learn how to apply established distributed systems patterns to machine learning projects, and explore new ML-specific patterns as well. Firmly rooted in the real world, this book demonstrates how to apply patterns using examples based in TensorFlow, Kubernetes, Kubeflow, and Argo Workflows. Real-world scenarios, hands-on projects, and clear, practical DevOps techniques let you easily launch, manage, and monitor cloud-native distributed machine learning pipelines.

about the technology

Scaling up models from standalone devices to large distributed clusters is one of the biggest challenges faced by modern machine learning practitioners. Distributing machine learning systems allow developers to handle extremely large datasets across multiple clusters, take advantage of automation tools, and benefit from hardware accelerations. In this book, Kubeflow co-chair Yuan Tang shares patterns, techniques, and experience gained from years spent building and managing cutting-edge distributed machine learning infrastructure.

about the book

Distributed Machine Learning Patterns is filled with practical patterns for running machine learning systems on distributed Kubernetes clusters in the cloud. Each pattern is designed to help solve common challenges faced when building distributed machine learning systems, including supporting distributed model training, handling unexpected failures, and dynamic model serving traffic. Real-world scenarios provide clear examples of how to apply each pattern, alongside the potential trade offs for each approach. Once you''ve mastered these cutting edge techniques, you''ll put them all into practice and finish up by building a comprehensive distributed machine learning system.
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Tjek vores konkurrenters priser
Practical patterns for scaling machine learning from your laptop to a distributed cluster.

In  Distributed Machine Learning Patterns you will learn how to:

  • Apply distributed systems patterns to build scalable and reliable machine learning projects
  • Construct machine learning pipelines with data ingestion, distributed training, model serving, and more
  • Automate machine learning tasks with Kubernetes, TensorFlow, Kubeflow, and Argo Workflows
  • Make trade offs between different patterns and approaches
  • Manage and monitor machine learning workloads at scale
Scaling up models from standalone devices to large distributed clusters is one of the biggest challenges faced by modern machine learning practitioners. Distributed Machine Learning Patterns teaches you how to scale machine learning models from your laptop to large distributed clusters. 

In Distributed Machine Learning Patterns, you''ll learn how to apply established distributed systems patterns to machine learning projects, and explore new ML-specific patterns as well. Firmly rooted in the real world, this book demonstrates how to apply patterns using examples based in TensorFlow, Kubernetes, Kubeflow, and Argo Workflows. Real-world scenarios, hands-on projects, and clear, practical DevOps techniques let you easily launch, manage, and monitor cloud-native distributed machine learning pipelines

Distributed Machine Learning Patterns teaches you how to scale machine learning models from your laptop to large distributed clusters. In it, you''ll learn how to apply established distributed systems patterns to machine learning projects, and explore new ML-specific patterns as well. Firmly rooted in the real world, this book demonstrates how to apply patterns using examples based in TensorFlow, Kubernetes, Kubeflow, and Argo Workflows. Real-world scenarios, hands-on projects, and clear, practical DevOps techniques let you easily launch, manage, and monitor cloud-native distributed machine learning pipelines.

about the technology

Scaling up models from standalone devices to large distributed clusters is one of the biggest challenges faced by modern machine learning practitioners. Distributing machine learning systems allow developers to handle extremely large datasets across multiple clusters, take advantage of automation tools, and benefit from hardware accelerations. In this book, Kubeflow co-chair Yuan Tang shares patterns, techniques, and experience gained from years spent building and managing cutting-edge distributed machine learning infrastructure.

about the book

Distributed Machine Learning Patterns is filled with practical patterns for running machine learning systems on distributed Kubernetes clusters in the cloud. Each pattern is designed to help solve common challenges faced when building distributed machine learning systems, including supporting distributed model training, handling unexpected failures, and dynamic model serving traffic. Real-world scenarios provide clear examples of how to apply each pattern, alongside the potential trade offs for each approach. Once you''ve mastered these cutting edge techniques, you''ll put them all into practice and finish up by building a comprehensive distributed machine learning system.
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Produktdetaljer
Sprog: Engelsk
Sider: 375
ISBN-13: 9781617299025
Indbinding: Paperback
Udgave:
ISBN-10: 1617299022
Kategori: Machine learning
Udg. Dato: 17 jan 2024
Længde: 16mm
Bredde: 236mm
Højde: 188mm
Forlag: Manning Publications
Oplagsdato: 17 jan 2024
Forfatter(e): Yuan Tang
Forfatter(e) Yuan Tang

Kategori Machine learning

ISBN-13 9781617299025

Sprog Engelsk

Indbinding Paperback

Sider 375

Udgave

Længde 16mm

Bredde 236mm

Højde 188mm

Udg. Dato 17 jan 2024

Oplagsdato 17 jan 2024

Forlag Manning Publications