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Pipeline orchestration
Artifact tracking

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.

Example stacks

Airflow + MLflow stack