• 1

    The configuration

    You can adapt the configuration to choose the platforms and services that you want to use for each stage of the ML workflow: data preparation, model training, prediction serving, and service management.

  • 2


    You can choose to deploy your Kubernetes workloads locally, on-premises, or to a cloud environment.

  • 3


    Easy, repeatable, portable deployments on a diverse infrastructure.