Distributed Machine Learning Patterns
A book teaching practical patterns for building scalable and reliable distributed machine learning systems using Kubernetes, TensorFlow, Kubeflow, and Argo Workflows.
What is Distributed Machine Learning Patterns?
Distributed Machine Learning Patterns is a repository containing references and code for the book of the same name, which teaches how to scale machine learning models from personal devices to large distributed clusters. It provides practical patterns and techniques for building production-ready, cloud-native ML systems, addressing common challenges like distributed training, failure handling, and dynamic serving.
Data analysts, data scientists, and software engineers familiar with the basics of machine learning algorithms and running ML in production, who need to design scalable distributed systems. Readers should have basic knowledge of Bash, Python, and Docker.
Developers choose this for its pattern-based approach to designing distributed ML systems, offering repeatable solutions that balance scalability, reliability, and maintainability. It provides real-world examples and hands-on guidance using industry-standard tools like Kubernetes, Kubeflow, and Argo Workflows directly from a key maintainer and contributor.
Overview
Distributed Machine Learning Patterns from Manning Publications by Yuan Tang https://bit.ly/2RKv8Zo
Use Cases
Best For
Related Projects
Found a gem we're missing?
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
