Data Science

Databricks using Azure ML model registry

Databricks using Azure ML model registry

Registering a model in AzureML from Databricks

What if we want to use AzureML as a central model repository for all models across many platforms (in this case Databricks workspaces). The AzureML model registry also uses MLFlow. I will follow these docs and show it step by step with examples.


Get things ready

There will be a few secrets we will need to maintain to get this going. The authentication we will use from Databricks to AzureML will be using an Azure Service Principal.

Create a Service Principal

Firstly lets create a Service Principal in our Azure Active Directory. Using the Azure PowerShell module:

az ad sp create-for-rbac --sdk-auth --name ml-auth --role Contributor --scopes /subscriptions/<subid>

You’ll see output like the following JSON:

  "clientId": "<guid>",
  "clientSecret": "<guid>",
  "subscriptionId": "<guid>",
  "tenantId": "<guid>",
  "activeDirectoryEndpointUrl": "",
  "resourceManagerEndpointUrl": "",
  "activeDirectoryGraphResourceId": "",
  "sqlManagementEndpointUrl": "",
  "galleryEndpointUrl": "",
  "managementEndpointUrl": ""

Create the secret scope

Using this output we will put the bits we will need in a Databricks secret scope. If you don’t know how to do this using the Databricks cli you can follow this Databricks CLI. The main thing is to install it using pip and then configure it to authenticate to your workspace using databricks configure -t <databricks-pat>. Once you have done this you need to create a secret scope (I called mine azureml).

Store the secrets

databricks secrets put --scope azureml --key clientId
databricks secrets put --scope azureml --key clientSecret
databricks secrets put --scope azureml --key subscriptionId
databricks secrets put --scope azureml --key tenantId

Let’s also store some of the other info we need in the same secret scope while we are at it.

databricks secrets put --scope azureml --key subscriptionid
databricks secrets put --scope azureml --key resourcegroup
databricks secrets put --scope azureml --key workspacename

Configure the external model registry

Now to configure your Databricks cluster to use the external MLFlow model registry you need to add this extra cell in your notebook:

# Change the MLFlow tracking URI to the AzureML one
import os
from import MLClient
import azure.identity as azurei
import mlflow

# set environment variables for the service principal authentication of DefaultAzureCredential() getting details from the secret scope
os.environ["AZURE_CLIENT_ID"] = dbutils.secrets.get(scope = "azureml", key = "clientId")
os.environ["AZURE_TENANT_ID"] = dbutils.secrets.get(scope = "azureml", key = "tenantId")
os.environ["AZURE_CLIENT_SECRET"] = dbutils.secrets.get(scope = "azureml", key = "clientSecret")

# Enter details of your AzureML workspace getting details from the secret scope
subscription_id = dbutils.secrets.get(scope = "azureml", key = "subscriptionid")
resource_group = dbutils.secrets.get(scope = "azureml", key = "resourcegroup")
workspace_name = dbutils.secrets.get(scope = "azureml", key = "workspacename")

ml_client = MLClient(

azureml_mlflow_uri = ml_client.workspaces.get(workspace_name).mlflow_tracking_uri

This should output the URI of your AzureML model registry.


Train you model

Now, when we log any artifacts using MLFlow within Databricks, it will be logged against the MLFlow registry in AzureML.

mlflow.spark.log_model(spark_model=model, artifact_path="model")

Experiment list:
azureml experiment

Specific experiment:
azureml experiment nfl

Model list:
azureml model Specific model:
azureml model nfl