MLflow Model Registry

The MLflow Model Registry component is a centralized model store, set of APIs, and UI, to collaboratively manage the full lifecycle of an MLflow Model. It provides model lineage (which MLflow experiment and run produced the model), model versioning, stage transitions (for example from staging to production), and annotations.

Table of Contents

Concepts
Adding an MLflow Model to the Model Registry

Concepts

The MLflow Model Registry is a standalone component that introduces several concepts that describe the full lifecycle of an MLflow Model.

Model: An MLflow Model logged with one of the model flavor’s log_model methods.
Registered Model: An MLflow Model registered with the MLflow Model Registry. A registered model has a unique name, contains versions, and other metadata.
Model Version: Each registered model can have one or many versions. When a new model is added to the Model Registry, it is added as version 1. Each new model registered to the same model name increments the version number.
Model Stage: Each model version can be assigned one or many stages. MLflow provides predefined stages for common use-cases such as Staging and Production.

Adding an MLflow Model to the Model Registry

Before a model can be added to the Model Registry you must log it using the log_model methods of the corresponding model flavors. Once a model has been logged, you can add it to the registry either through a UI workflow or through APIs.

UI Workflow

From the MLflow Runs detail page, select a logged MLflow Model in the Artifacts section. Click the Register Model button.

In the Model Name field, if you are adding a new model, specify a unique name to identify the model. If you are registering a new version to an existing model, pick the existing model name from the dropdown.

Once the model is added to the Model Registry you can:

Navigate to the Registered Models page and view the model properties.
Go to the Artifacts section of the run detail page, click the model, and then click the model version at the top right to view the version you just created.

Each model has an overview page that shows the active versions. Click a version to navigate to the version detail page.

On the version detail page you can see model version details and the current stage of the model version. If you click on the stage in the top right, you can transition the model version to one of the other valid stages.

API Workflow

Once a model is logged using log_model you can add it to the registry, referencing the model by its URI.

Call the register_model API to add it to the registry.

mlflow.register_model("runs:/e148278107274..832/artifacts/model",
                      "EmailMarketingCampaign")

Alternatively, you can pass registered_model_name to log_model to log the model under the specified registered model.

mlflow.tensorflow.log_model(tf_saved_model_dir=saved_estimator_path,
                            tf_meta_graph_tags=[tag_constants.SERVING],
                            tf_signature_def_key="predict",
                            artifact_path="model",
                            registered_model_name="EmailMarketingCampaign")

Note

If the registered model does not already exist, the registry will create one before creating a new model version.

With the get_model_version_details API you can confirm that the model has successfully been registered.

client = mlflow.tracking.MlflowClient()
client.get_model_version_details(name = "EmailMarketingCampaign",
                                 version = 12)

You can ask for the latest version of each registered model by a particular stage.

client.get_latest_versions(name = "EmailMarketingCampaign",
                           stages = ["Staging"])

Newly-registered model versions are added to the registry with stage None. You can update its stage.

client.update_model_version(name = "EmailMarketingCampaign",
                            version = 12
                            stage = "Production")