Arrikto Enterprise Kubeflow
The Arrikto Enterprise Kubeflow (EKF) distribution extends the capabilities of the Kubeflow platform with additional automation, reproducibility, portability, and security features.
- Automation: Orchestrate your end-to-end ML workflow with a click of a button. Start by tagging cells in Jupyter Notebooks to define pipeline steps, hyperparameter tuning, GPU usage, and metrics tracking. Click a button to create pipeline components and KFP DSL, resolve dependencies, inject data objects into each step, deploy the data science pipeline, and serve the best model. Or use the Kale SDK to do all the above with your preferred IDE.
- Reproducibility: Snapshot pipeline code, libraries, and data for every step with Arrikto’s Rok data management platform. Roll back to any machine learning pipeline step at it’s exact execution state for easy debugging. Collaborate with other data scientists through a Git-style publish/subscribe versioning workflow.
- Portability: Deploy and upgrade your Kubeflow environment with a proven GitOps process across all major public clouds, and on-prem infrastructure. Move ML workflows seamlessly across with Rok Registry.
- Security: Manage teams and user access via GitLab or any ID provider via Istio/OIDC. Isolate user ML data access within their own namespace while enabling notebook and pipeline collaboration in shared namespaces. Manage secrets and credentials securely, and efficiently.
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