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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.
sdk
Publictrainer
PublicDistributed ML Training and Fine-Tuning on Kubernetesnotebooks
Publicinternal-acls
Publicarena
PublicA CLI for Kubeflow.community
Publicspark-operator
PublicKubernetes operator for managing the lifecycle of Apache Spark applications on Kubernetes.katib
PublicAutomated Machine Learning on Kubernetesmpi-operator
PublicKubernetes Operator for MPI-based applications (distributed training, HPC, etc.)kubeflow
PublicMachine Learning Toolkit for Kubernetesblog
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 archivefairing
Public archivexgboost-operator
Public archivemxnet-operator
Public archive