graph that includes custom TRANSFORMER and ROUTER.If you are looking for a more easy way to deploy your models locally, you can use the MLflow Model Deployer flavor.

How to deploy it?

ZenML provides a Seldon Core flavor build on top of the Seldon Core Integration to allow you to deploy and use your models in a production-grade environment. In order to use the integration you need to install it on your local machine to be able to register a Seldon Core Model deployer with ZenML and add it to your stack:

zenml integration install seldon -y

To deploy and make use of the Seldon Core integration we need to have the following prerequisites:

access to a Kubernetes cluster. This can be configured using the kubernetes_context configuration attribute to point to a local kubectl context or an in-cluster configuration, but the recommended approach is to use a Service Connector to link the Seldon Deployer Stack Component to a Kubernetes cluster.

Seldon Core needs to be preinstalled and running in the target Kubernetes cluster. Check out the official Seldon Core installation instructions or the EKS installation example below.

models deployed with Seldon Core need to be stored in some form of persistent shared storage that is accessible from the Kubernetes cluster where Seldon Core is installed (e.g. AWS S3, GCS, Azure Blob Storage, etc.). You can use one of the supported remote artifact store flavors to store your models as part of your stack. For a smoother experience running Seldon Core with a cloud artifact store, we also recommend configuring explicit credentials for the artifact store. The Seldon Core model deployer knows how to automatically convert those credentials in the format needed by Seldon Core model servers to authenticate to the storage back-end where models are stored.

Since the Seldon Model Deployer is interacting with the Seldon Core model server deployed on a Kubernetes cluster, you need to provide a set of configuration parameters. These parameters are: