Practical patterns for scaling Machine Learning from your laptop to a distributed cluster. 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. For data analysts, data scientists, and software engineers who know the basics of Machine Learning algorithms and running Machine Learning in production. Readers should be familiar with the basics of Bash, Python, and Docker.