Registering a Model

Registering models can be done in a number of ways depending on your specific needs. You can explicitly register models using the CLI or the Python SDK, or you can just allow ZenML to implicitly register your models as part of a pipeline run.

If you are using ZenML Pro, you already have access to a dashboard interface that allows you to register models.

Explicit CLI registration

Registering models using the CLI is as straightforward as the following command:

zenml model register iris_logistic_regression --license=... --description=...

You can view some of the options of what can be passed into this command by running zenml model register --help but since you are using the CLI outside a pipeline run the arguments you can pass in are limited to non-runtime items. You can also associate tags with models at this point, for example, using the --tag option.

Explicit dashboard registration

ZenML Pro can register their models directly from the cloud dashboard interface.

Register a model on the [ZenML Pro](https://zenml.io/pro) dashboard

Explicit Python SDK registration

You can register a model using the Python SDK as follows:

from zenml import Model
from zenml.client import Client

Client().create_model(
    name="iris_logistic_regression",
    license="Copyright (c) ZenML GmbH 2023",
    description="Logistic regression model trained on the Iris dataset.",
    tags=["regression", "sklearn", "iris"],
)

Implicit registration by ZenML

The most common use case for registering models is to do so implicitly as part of a pipeline run. This is done by specifying a Model object as part of the model argument of the @pipeline decorator.

As an example, here we have a training pipeline which orchestrates the training of a model object, storing datasets and the model object itself as links within a newly created Model version. This integration is achieved by configuring the pipeline within a Model Context using Model. The name is specified, while other fields remain optional for this task.

from zenml import pipeline
from zenml import Model

@pipeline(
    enable_cache=False,
    model=Model(
        name="demo",
        license="Apache",
        description="Show case Model Control Plane.",
    ),
)
def train_and_promote_model():
    ...

Running the training pipeline creates a new model version, all while maintaining a connection to the artifacts.