Kubeflow

Kubeflow is an open-source, cloud-native platform that serves as the foundation for building AI platforms on Kubernetes. As a Cloud Native Computing Foundation (CNCF) project, it provides a comprehensive ecosystem of Kubernetes-native tools designed to support every stage of the AI lifecycle, from model development and training to deployment and serving. The platform has gained significant community traction with over 258 million PyPI downloads, 33,100+ GitHub stars, and contributions from more than 3,000 developers worldwide. The Kubeflow platform offers a modular, composable architecture that allows AI platform teams to either deploy individual components or implement the entire AI reference platform based on their specific requirements. Key projects include Kubeflow Pipelines for building portable ML workflows, Kubeflow Trainer for distributed training across frameworks like PyTorch and HuggingFace, KServe for scalable AI inference, Katib for automated machine learning, and Model Registry for managing model artifacts. This flexibility enables organizations to deploy Kubeflow anywhere Kubernetes runs, ensuring portability across cloud providers and on-premises environments. Backed by a vibrant open-source community with regular weekly calls, active mailing lists, and Slack discussions, Kubeflow continues to evolve with contributions from major technology organizations. The project is trusted by numerous enterprise adopters seeking to streamline their machine learning operations on Kubernetes infrastructure.