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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.
mpi-operator
PublicKubernetes 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.
- Distributed AI Model Training and Fine-Tuning on Kubernetes
internal-acls
Publicarena
PublicA CLI for Kubeflow.- Automated Machine Learning on Kubernetes
- Kubeflow blog based on fastpages
kubeflow
PublicMachine Learning Toolkit for Kubernetesexamples
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 engine.allstar
Public archivexgboost-operator
Public archive