ructure are stored and managed by the ZenML Cloud.On our infrastructure for ZenML Cloud only ML metadata (e.g. pipeline and model tracking and versioning information) is stored. All the actual ML data artifacts (e.g. data produced or consumed by pipeline steps, logs and visualizations, models) are stored on the customer cloud. This can be set up quite easily by configuring an artifact store with your MLOps stack.

Your tenant only needs permissions to read from this data to display artifacts on the ZenML dashboard. The tenant also needs direct access to parts of the customer infrastructure services to support dashboard control plane features such as CI/CD, triggering and running pipelines, triggering model deployments etc.

This scenario is meant for customers who want to quickly get started with ZenML and can to a certain extent allow ingress connections into their infrastructure from an external SaaS provider.

Scenario 2: Hybrid SaaS with Customer Secret Store managed by ZenML

This scenario is a version of Scenario 1. modified to store all sensitive information on the customer side. In this case, the customer connects their own secret store directly to the ZenML server that is managed by us. All ZenML secrets used by running pipelines to access infrastructure services and resources are stored in the customer secret store. This allows users to use service connectors and the secrets API to authenticate ZenML pipelines and the ZenML Cloud to 3rd party services and infrastructure while ensuring that credentials are always stored on the customer side.