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. 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.

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

Yuan Tang is a senior software engineer at Ant Group, where he works on AI infrastructure and AutoML platforms on Kubernetes. Yuan is co-chair of Kubeflow, top contributor to Argo Workflows, and committer on TensorFlow. He is the co-author of TensorFlow in Practice and author of the TensorFlow implementation of Dive into Deep Learning.