nml-step-name="trainer"

Deploy a registered modelAfter you have registered a model in the MLflow model registry, you can also easily deploy it as a prediction service. Checkout the MLflow model deployer documentation for more information on how to do that.

Interact with registered models

You can also use the ZenML CLI to interact with registered models and their versions.

The zenml model-registry models list command will list all registered models in the model registry:

$ zenml model-registry models list

┏━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━┓

┃          NAME          │ DESCRIPTION │ METADATA ┃

┠────────────────────────┼─────────────┼──────────┨

┃ tensorflow-mnist-model │             │          ┃

┗━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━┛

To list all versions of a specific model, you can use the zenml model-registry models list-versions REGISTERED_MODEL_NAME command:

$ zenml model-registry models list-versions tensorflow-mnist-model

┏━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓

┃          NAME          │ MODEL_VERSION │ VERSION_DESCRIPTION                     │ METADATA                                                                                                                                                                             ┃

┠────────────────────────┼───────────────┼─────────────────────────────────────────┼──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┨

┃ tensorflow-mnist-model │ 3             │ Run #3 of the mlflow_training_pipeline. │ {'zenml_version': '0.34.0', 'zenml_run_name': 'mlflow_training_pipeline-2023_03_01-08_09_23_672599', 'zenml_pipeline_name': 'mlflow_training_pipeline',                       ┃