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- Machine Learning Pipelines for Kubeflow
model-registry
PublicModel 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.- Kubeflow’s superfood for Data Scientists
- Distributed AI Model Training and Fine-Tuning on Kubernetes
- MCP Server for Apache Spark History Server. The bridge between Agentic AI and Apache Spark.
- Kubernetes operator for managing the lifecycle of Apache Spark applications on Kubernetes.
- Universal Python SDK to run AI workloads on Kubernetes
- Kubernetes Operator for MPI-based applications (distributed training, HPC, etc.)
dashboard
Publicarena
PublicA CLI for Kubeflow.kubeflow
PublicMachine Learning Toolkit for Kubernetes- Automated Machine Learning on Kubernetes
manifests
Publicblog
PublicKubeflow blog based on fastpagesdocs-agent
Publicexamples
Publictesting
Publickfp-tekton
Public archiveKubeflow Pipelines on Tektonkfserving-lts
Public archivekubebench
Public archivefate-operator
Public archivekfctl
Public archivekfp-tekton-backend
Public archiveExperimental project plugging Tekton yaml behind KFP API and UI enginecommon
Public archive.allstar
Public archive