Kubeflow

Kubeflow makes deploying ML workflows on Kubernetes simple, portable and scalable.

Use it when

• •You want an opinionated pipeline orchestration toolbox that is focused on ML-specific workloads on Kubernetes.
• •You want a tool that is cloud provider agnostic.
• •You want a framework that integrates all components to cover each phase of the ML lifecycle.
• •You want to run Jupyter Notebooks on GPU instances with shared data backends.
• •You want to autoscale compute resources to your workload needs.
• •You want to deploy ML models to production.

Watch out

• ⚠Extensive configuration options require significant expertise and experimentation to get the optimal configuration.
• ⚠Reliability issues may arise from component dependencies and their version incompatibilities. Updating one component might break other parts due to incompatibilities.
• ⚠Kubeflow expects that containers are in cloud container registries.

Available in stages

Example stacks