other remote stack components also running in GCP.This method uses the implicit GCP authentication available in the environment where the ZenML code is running. On your local machine, this is the quickest way to configure a GCS Artifact Store. You don't need to supply credentials explicitly when you register the GCS Artifact Store, as it leverages the local credentials and configuration that the Google Cloud CLI stores on your local machine. However, you will need to install and set up the Google Cloud CLI on your machine as a prerequisite, as covered in the Google Cloud documentation , before you register the GCS Artifact Store.

Certain dashboard functionality, such as visualizing or deleting artifacts, is not available when using an implicitly authenticated artifact store together with a deployed ZenML server because the ZenML server will not have permission to access the filesystem.

The implicit authentication method also needs to be coordinated with other stack components that are highly dependent on the Artifact Store and need to interact with it directly to the function. If these components are not running on your machine, they do not have access to the local Google Cloud CLI configuration and will encounter authentication failures while trying to access the GCS Artifact Store:

Orchestrators need to access the Artifact Store to manage pipeline artifacts

Step Operators need to access the Artifact Store to manage step-level artifacts

Model Deployers need to access the Artifact Store to load served models

To enable these use cases, it is recommended to use a GCP Service Connector to link your GCS Artifact Store to the remote GCS bucket.

To set up the GCS Artifact Store to authenticate to GCP and access a GCS bucket, it is recommended to leverage the many features provided by the GCP Service Connector such as auto-configuration, best security practices regarding long-lived credentials and reusing the same credentials across multiple stack components.