l version is associated with a model registration.ModelVersionStage: A model version stage is a state in that a model version can be. It can be one of the following: None, Staging, Production, Archived. The model version stage is used to track the lifecycle of a model version. For example, a model version can be in the Staging stage while it is being tested and then moved to the Production stage once it is ready for deployment.

When to use it

ZenML provides a built-in mechanism for storing and versioning pipeline artifacts through its mandatory Artifact Store. While this is a powerful way to manage artifacts programmatically, it can be challenging to use without a visual interface.

Model registries, on the other hand, offer a visual way to manage and track model metadata, particularly when using a remote orchestrator. They make it easy to retrieve and load models from storage, thanks to built-in integrations. A model registry is an excellent choice for interacting with all the models in your pipeline and managing their state in a centralized way.

Using a model registry in your stack is particularly useful if you want to interact with all the logged models in your pipeline, or if you need to manage the state of your models in a centralized way and make it easy to retrieve, load, and deploy these models.

How model registries fit into the ZenML stack

Here is an architecture diagram that shows how a model registry fits into the overall story of a remote stack.

Model Registry Flavors

Model Registries are optional stack components provided by integrations:

Model Registry Flavor Integration Notes MLflow mlflow mlflow Add MLflow as Model Registry to your stack Custom Implementation custom custom

If you would like to see the available flavors of Model Registry, you can use the command:

zenml model-registry flavor list

How to use it