Kubeflow SDK v0.4.0 introduces a ModelRegistryClient for managing model artifacts and versions via a Pythonic API, a SparkClient with SparkConnect support for interactive distributed data processing on Kubernetes without YAML, namespaced TrainingRuntimes for better multi-tenant isolation, and Dataset/Model Initializers to improve parity between local and remote execution. The release also raises the minimum Python version to 3.10 and launches a dedicated documentation website. Future roadmap items include MCP server integration, MLflow support, Kubeflow Pipelines unification, LLM training, and multi-cluster job submission.
Table of contents
Unified Model Management: The Model Registry ClientDistributed AI Data at Scale: SparkClient & SparkConnectA New Home for DocumentationInfrastructure & Breaking ChangesWhat’s Next for Kubeflow SDKGet Involved!Sort: