Wallaroo users can register their trained machine learning models from a model registry into their Wallaroo instance and perform inferences with it through a Wallaroo pipeline.

The following details procedures for DevOps Engineers and MLOps Engineers creating a model or artifact registry and connect it to Wallaroo.

Registry Services Roles

Registry service use in Wallaroo typically falls under the following roles.

Model Registry Operations

The following links to guides and information on setting up a model registry (also known as an artifact registry).

Create Model Registry

• Model Registry
• Azure Databricks
• MLflow Model Registry

See Model serving with Azure Databricks for setting up a model registry service using Azure Databricks.

The following steps create an Access Token used to authenticate to an Azure Databricks Model Registry.

1. Log into the Azure Databricks workspace.
2. From the upper right corner access the User Settings.
3. From the Access tokens, select Generate new token.
4. Specify any token description and lifetime. Once complete, select Generate.
5. Copy the token and store in a secure place. Once the Generate New Token module is closed, the token will not be retrievable.

The MLflow Model Registry provides a method of setting up a model registry service. Full details can be found at the MLflow Registry Guides.

A generic MLFlow model registry requires no token.

Wallaroo Registry Operations

• Connect Model Registry to Wallaroo: This details the link and connection information to a existing MLFlow registry service. Note that this does not create a MLFlow registry service, but adds the connection and credentials to Wallaroo to allow that MLFlow registry service to be used by other entities in the Wallaroo instance.

As of this time, Wallaroo only supports MLFlow Registry version 1.30, and only supports deploying model artifacts that meet the Wallaroo model versions.

Connect Model Registry to Wallaroo

Registry connection information is added to a Wallaroo instance through the Wallaroo.Client.create_model_registry method.

Connect Model Registry to Wallaroo Parameters

Connect Model Registry to Wallaroo Return

The following is returned when a MLFlow Registry is successfully created.

Note that the token is not displayed for security reasons.

Connect Model Registry to Wallaroo Example

The following example creates a Wallaroo MLFlow Registry with the name ExampleNotebook stored in a sample Azure DataBricks environment.

wl.create_model_registry(name="ExampleNotebook", 
                        token="abcdefg-3", 
                        url="https://abcd-123489.456.azuredatabricks.net")

Add Registry to Workspace

Registries are assigned to a Wallaroo workspace with the Wallaroo.registry.add_registry_to_workspace method. This allows members of the workspace to access the registry connection. A registry can be associated with one or more workspaces.

Add Registry to Workspace Parameters

Add Registry to Workspace Returns

The following is returned when a MLFlow Registry is successfully added to a workspace.

Example

registry.add_registry_to_workspace(workspace_id=workspace_id)

Remove Registry from Workspace

Registries are removed from a Wallaroo workspace with the Registry remove_registry_from_workspace method.

Remove Registry from Workspace Parameters

Remove Registry from Workspace Return

Remove Registry from Workspace Example

registry.remove_registry_from_workspace(workspace_id=workspace_id)