om your ZenML steps.

List of available parametersWhen using the mlflow_register_model_step, you can set a variety of parameters for fine-grained control over which information is logged with your model:

name: The name of the model. This is a required parameter.

version: version: The version of the model.

trained_model_name: Name of the model artifact in MLflow.

model_source_uri: The path to the model. If not provided, the model will be fetched from the MLflow tracking server via the trained_model_name.

description: A description of the model version.

metadata: A list of metadata to associate with the model version.

The model_source_uri parameter is the path to the model within the MLflow tracking server.

If you are using a local MLflow tracking server, the path will be something like file:///.../mlruns/667102566783201219/3973eabc151c41e6ab98baeb20c5323b/artifacts/model.

If you are using a remote MLflow tracking server, the path will be something like s3://.../mlruns/667102566783201219/3973eabc151c41e6ab98baeb20c5323b/artifacts/model.

You can find the path of the model in the MLflow UI. Go to the Artifacts tab of the run that produced the model and click on the model. The path will be displayed in the URL:

Register models via the CLI

Sometimes adding a mlflow_registry_training_pipeline step to your pipeline might not be the best option for you, as it will register a model in the MLflow model registry every time you run the pipeline.

If you want to register your models manually, you can use the zenml model-registry models register-version CLI command instead:

zenml model-registry models register-version Tensorflow-model \

--description="A new version of the tensorflow model with accuracy 98.88%" \

v 1 \

--model-uri="file:///.../mlruns/667102566783201219/3973eabc151c41e6ab98baeb20c5323b/artifacts/model" \

m key1 value1 -m key2 value2 \

--zenml-pipeline-name="mlflow_training_pipeline" \

--zenml-step-name="trainer"

Deploy a registered model