Kubeflow offers ML model development environments optimized for cloud, enabling Kubernetes-based linking with various open source software.
The standardized environments support a range of machine learning frameworks from TensorFlow, PyTorch, scikit-learn, and Keras. The pipeline for the entire development, learning and deployment processes of machine learning models are automated to ensure simple configuration/creation as well as reuse of the models.
Expanded open source Kubeflow from distributed learning job execution/monitoring to inference service management/analysis and job queue management are provided on Samsung Cloud Platform. Users can also enjoy job schedulers (FIFO, Bin-packing, and Gang-based), GPU fraction, GPU resource monitoring, Kubeflow engine logging and more add-on features that are usually unavailable on open source software.
- Create AI platform (auto-deployment/configuration), view (platform version, resource status), and delete
- Provide Jupyter Notebook : Model development, learning, inference
- Automate machine learning pipeline workflow
- Provide other open source Kubeflow default feature
- Advanced AI/ML platform dashboard
- AI/ML notebook server : Base image, user-defined image
- AI/ML job : Job creation, template, archive, scheduling, execution, monitoring
※ Support GPU resource monitoring, GPU fraction
- Build and manage user image
- AI JumpStarter and ETM (Experiment Tracking Management)
- Serving : Dashboard, register/manage model, inference, predictions, and visualization
- Managing platform resource : Manage resource usage by project, monitor resource usage
- Manage project user/permissions, admin feature, adjust platform configuration
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