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