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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.
notebooks
Public- Machine Learning Pipelines for Kubeflow
internal-acls
Publicarena
PublicA CLI for Kubeflow.dashboard
Public- Kubernetes operator for managing the lifecycle of Apache Spark applications on Kubernetes.
sdk
PublicUniversal Python SDK to run AI workloads on Kubernetes- Distributed AI Model Training and Fine-Tuning on Kubernetes
- MCP Server for Apache Spark History Server. The bridge between Agentic AI and Apache Spark.
- Automated Machine Learning on Kubernetes
- Kubernetes Operator for MPI-based applications (distributed training, HPC, etc.)
blog
PublicKubeflow blog based on fastpages- Machine Learning Toolkit for Kubernetes
- Kubeflow Pipelines on Tekton
kfserving-lts
Public archivefate-operator
Public archivekfp-tekton-backend
Public archiveExperimental project plugging Tekton yaml behind KFP API and UI enginefairing
Public archivexgboost-operator
Public archive