Google Cloud Storage (GCS)

Storing artifacts using GCP Cloud Storage.

The GCS Artifact Store is an Artifact Store flavor provided with the GCP ZenML integration that uses the Google Cloud Storage managed object storage service to store ZenML artifacts in a GCP Cloud Storage bucket.

When would you want to use it?

Running ZenML pipelines with the local Artifact Store is usually sufficient if you just want to evaluate ZenML or get started quickly without incurring the trouble and the cost of employing cloud storage services in your stack. However, the local Artifact Store becomes insufficient or unsuitable if you have more elaborate needs for your project:

if you want to share your pipeline run results with other team members or stakeholders inside or outside your organization

if you have other components in your stack that are running remotely (e.g. a Kubeflow or Kubernetes Orchestrator running in a public cloud).

if you outgrow what your local machine can offer in terms of storage space and need to use some form of private or public storage service that is shared with others

if you are running pipelines at scale and need an Artifact Store that can handle the demands of production-grade MLOps

In all these cases, you need an Artifact Store that is backed by a form of public cloud or self-hosted shared object storage service.

You should use the GCS Artifact Store when you decide to keep your ZenML artifacts in a shared object storage and if you have access to the Google Cloud Storage managed service. You should consider one of the other Artifact Store flavors if you don't have access to the GCP Cloud Storage service.

How do you deploy it?

The GCP artifact store (and GCP integration in general) currently only works for Python versions <3.11. The ZenML team is aware of this dependency clash/issue and is working on a fix. For now, please use Python <3.11 together with the GCP integration.