English | 2022 | ISBN: 9781617299025 | 172 pages | True PDF | 8.33 MB

Practical patterns for scaling machine learning from your laptop to a distributed cluster.

Scaling up models from standalone devices to large distributed clusters is one of the biggest challenges faced by modern machine learning practitioners.<i>Distributed Machine Learning Patterns</i> teaches you how to scale machine learning models from your laptop to large distributed clusters.

In<i>Distributed Machine Learning Patterns</i>, 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.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.