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- Machine Learning Pipelines for Kubeflow
- Model Registry provides a single pane of glass for ML model developers to index and manage models, versions, and ML artifacts metadata. It fills a gap between model experimentation and production activities. It provides a central interface for all stakeholders in the MLOps lifecycle to collaborate on ML models.
- Universal Python SDK to run AI workloads on Kubernetes
- Distributed AI Model Training and Fine-Tuning on Kubernetes
- Kubernetes Operator for MPI-based applications (distributed training, HPC, etc.)
- Kubernetes operator for managing the lifecycle of Apache Spark applications on Kubernetes.
- MCP Server for Apache Spark History Server. The bridge between Agentic AI and Apache Spark.
- Automated Machine Learning on Kubernetes
arena
PublicA CLI for Kubeflow.blog
PublicKubeflow blog based on fastpagespipelines-components
Publiccommunity
Public- Machine Learning Toolkit for Kubernetes
testing
Public- Kubeflow Pipelines on Tekton
kubebench
Public archivekfctl
Public archive- Experimental project plugging Tekton yaml behind KFP API and UI engine