distributed-ml-patterns

Why we included this project

This repo is the companion code and reference material for Yuan Tang's Manning book on distributed machine learning. For data scientists and ML engineers who have outgrown single-machine training and are moving onto Kubernetes-backed clusters, it walks through the recurring patterns behind data ingestion, distributed training, model serving, and workflow automation, with examples built on TensorFlow, Kubeflow, and Argo Workflows. The code is organized by chapter, so you can work through the book's scenarios and see each pattern applied to a real pipeline rather than described in the abstract. Since the author maintains Argo, Kubeflow, and KServe, the examples reflect how these systems behave in production, not idealized textbook setups. Treat it as a guided tour and reference for building cloud-native ML infrastructure, not as something you deploy directly.