onent to a remote resource via a Service ConnectorThe following operations are only possible with Service Connector Types that are locally available (with some notable exceptions covered in the information box that follows):

Service Connector auto-configuration and discovery of credentials stored by a local client, CLI, or SDK (e.g. aws or kubectl).

Using the configuration and credentials managed by a Service Connector to configure a local client, CLI, or SDK (e.g. docker or kubectl).

Running pipelines with a Stack Component that is connected to a remote resource through a Service Connector

One interesting and useful byproduct of the way cloud provider Service Connectors are designed is the fact that you don't need to have the cloud provider Service Connector Type available client-side to be able to access some of its resources. Take the following situation for example:

the GCP Service Connector Type can provide access to GKE Kubernetes clusters and GCR Docker container registries.

however, you don't need the GCP Service Connector Type or any GCP libraries to be installed on the ZenML clients to connect to and use those Kubernetes clusters or Docker registries in your ML pipelines.

the Kubernetes Service Connector Type is enough to access any Kubernetes cluster, regardless of its provenance (AWS, GCP, etc.)

the Docker Service Connector Type is enough to access any Docker container registry, regardless of its provenance (AWS, GCP, etc.)

Register Service Connectors

When you reach this section, you probably already made up your mind about the type of infrastructure or cloud provider that you want to use to run your ZenML pipelines after reading through the Service Connector Types section, and you probably carefully weighed your choices of authentication methods and best security practices. Either that or you simply want to quickly try out a Service Connector to connect one of the ZenML Stack components to an external resource.