Distributed Machine Learning Patterns - Tang, Yuan
59,99 €
inkl. MwSt.
Versandkostenfrei*
Versandfertig in 2-4 Wochen
payback
30 °P sammeln
  • Broschiertes Buch

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,…mehr

Andere Kunden interessierten sich auch für
Produktbeschreibung
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. Distributing machine learning systems allow developers to handle extremely large datasets across multiple clusters, take advantage of automation tools, and benefit from hardware accelerations.
Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Autorenporträt
Yuan Tang is currently a founding engineer at Akuity. Previously he was a senior software engineer at Alibaba Group, building AI infrastructure and AutoML platforms on Kubernetes. Yuan is co-chair of Kubeflow, maintainer of Argo, TensorFlow, XGBoost, and Apache MXNet. He is the co-author of TensorFlow in Practice and author of the TensorFlow implementation of Dive into Deep Learning.