of configuration parameters. These parameters are:kubernetes_context: the Kubernetes context to use to contact the remote Seldon Core installation. If not specified, the active Kubernetes context is used or the in-cluster configuration is used if the model deployer is running in a Kubernetes cluster. The recommended approach is to use a Service Connector to link the Seldon Deployer Stack Component to a Kubernetes cluster and to skip this parameter.

kubernetes_namespace: the Kubernetes namespace where the Seldon Core deployment servers are provisioned and managed by ZenML. If not specified, the namespace set in the current configuration is used.

base_url: the base URL of the Kubernetes ingress used to expose the Seldon Core deployment servers.

In addition to these parameters, the Seldon Core Model Deployer may also require additional configuration to be set up to allow it to authenticate to the remote artifact store or persistent storage service where model artifacts are located. This is covered in the Managing Seldon Core Authentication section.

Configuring Seldon Core in a Kubernetes cluster can be a complex and error-prone process, so we have provided a set of Terraform-based recipes to quickly provision popular combinations of MLOps tools. More information about these recipes can be found in the MLOps Stack Recipes.

Infrastructure Deployment

The Seldon Model Deployer can be deployed directly from the ZenML CLI:

zenml model-deployer deploy seldon_deployer --flavor=seldon --provider=<YOUR_PROVIDER> ...

You can pass other configurations specific to the stack components as key-value arguments. If you don't provide a name, a random one is generated for you. For more information about how to work use the CLI for this, please refer to the dedicated documentation section.

Seldon Core Installation Example