Custom machine learning models


Support for Machine Learning Studio (classic) will end on 31 August 2024. We recommend you transition to Azure Machine Learning by that date.

Beginning 1 December 2021, you will not be able to create new Machine Learning Studio (classic) resources. Through 31 August 2024, you can continue to use the existing Machine Learning Studio (classic) resources. For more information, see Migrate to Azure Machine Learning.

Custom models lets you manage workflows based on Azure Machine Learning models. Workflows help you choose the data you want to generate insights from and map the results to your unified customer data. For more information about building custom ML models, see Use Azure Machine Learning-based models.


  • Web services published through Azure Machine Learning batch pipelines.

  • Pipeline must be published under a pipeline endpoint.

  • An Azure Data Lake Gen2 storage account associated with your Azure Studio instance.

  • For Azure Machine Learning workspaces with pipelines, Owner or User Access Administrator permissions to the Azure Machine Learning Workspace.


    Data is transferred between your Customer Insights instances and the selected Azure web services or pipelines in the workflow. When you transfer data to an Azure service, please ensure that service is configured to process data in the manner and location necessary to comply with any legal or regulatory requirements for that data for your organization.

Add a new workflow

  1. Go to Intelligence > Custom models and select New workflow.

  2. Provide a recognizable Name.

    Screenshot of the New workflow pane.

  3. Select the organization that contains the web service in Tenant that contains your web service.

  4. If your Azure Machine Learning subscription is in a different tenant than Customer Insights, select Sign in with your credentials for the selected organization.

  5. Select the Workspaces associated with your web service.

  6. Choose the Azure Machine Learning pipeline in the Web service that contains your model dropdown. Then, select Next. Learn more about publishing a pipeline in Azure Machine Learning using the designer or SDK.

  7. For each Web service input, select the matching Entity from Customer Insights. Then, select Next.


    The custom model workflow will apply heuristics to map the web service input fields to the entity attributes based on the name and data type of the field. You'll see an error if a web service field can't be mapped to an entity.

    Configure a workflow.

  8. For Model Output Parameters, set the following properties:

    • Entity name for the pipeline output results
    • Output data store parameter name of your batch pipeline
    • Output Path parameter name of your batch pipeline

    Model Output Parameter Pane.

  9. Select the matching attribute from Customer ID in results that identifies customers and select Save.

    Relate results to Customer data pane.

    The Workflow Saved screen displays details about the workflow. If you configured a workflow for an Azure Machine Learning pipeline, Customer Insights attaches to the workspace that contains the pipeline. Customer Insights will get a Contributor role on the Azure workspace.

  10. Select Done. The Custom Models page displays.

  11. Select the vertical ellipsis (⋮) for the workflow and select Run. Your workflow also runs automatically with every scheduled refresh.

Manage an existing workflow

Go to Intelligence > Custom models to view the workflows you created.

Select a workflow to view available actions.

  • Edit a workflow
  • Run a workflow
  • Delete a workflow

Edit a workflow

  1. Go to Intelligence > Custom models.

  2. Next to the workflow you'd like to update, select the vertical ellipsis (⋮) and select Edit.

  3. Change Display name if needed, and select Next.

  4. For each Web service input, update the matching Entity from Customer Insights, if needed. Then, select Next.

  5. For Model Output Parameters, change any of the following:

    • Entity name for the pipeline output results
    • Output data store parameter name of your batch pipeline
    • Output Path parameter name of your batch pipeline
  6. Change the matching attribute from Customer ID in results to identify customers. Choose an attribute from the inference output with values similar to the Customer ID column of the Customer entity. If you don't have such a column in your data set, choose an attribute that uniquely identifies the row.

  7. Select Save

Delete a workflow

  1. Go to Intelligence > Custom models.

  2. Next to the workflow you'd like to delete, select the vertical ellipsis (⋮) and select Delete.

  3. Confirm your deletion.

Your workflow will be deleted. The entity that was created when you created the workflow persists, and can be viewed from the Data > Entities page.

View the results

Results from a workflow are stored in the entity name defined for Model Output Parameters. Access this data from the Data > Entities page or with API access.

API Access

For the specific OData query to get data from a custom model entity, use the following format:<your instance id>/data/<custom model output entity name>%3Ffilter%3DCustomerId%20eq%20'<guid value>'

  1. Replace <your instance id> with the ID of your Customer Insights environment, which displays in the address bar of your browser when accessing Customer Insights.

  2. Replace <custom model output entity> with the entity name you provided for the Model Output Parameters.

  3. Replace <guid value> with the Customer ID of the customer you'd like to access. This ID displays on the customer profiles page in the CustomerID field.

Frequently Asked Questions

  • Why can't I see my pipeline when setting up a custom model workflow? This issue is frequently caused by a configuration issue in the pipeline. Ensure the input parameter is configured, and the output datastore and path parameters are also configured.

  • What does the error "Couldn't save intelligence workflow" mean? Users usually see this error message if they don't have Owner or User Access Administrator privileges on the workspace. The user needs a higher level of permissions to enable Customer Insights to process the workflow as a service rather than using the user credentials for subsequent runs of the workflow.

  • Is a private endpoint / private link for my custom model workflow supported? Customer Insights does not currently support private endpoint for custom models out of the box, but a manual workaround is available. Please contact support for details.

Responsible AI

Predictions offer capabilities to create better customer experiences, improve business capabilities, and revenue streams. We strongly recommend you balance the value of your prediction against the impact it has and biases that may be introduced in an ethical manner. Learn more about how Microsoft is addressing Responsible AI. You can also learn about techniques and processes for responsible machine learning specific to Azure Machine Learning.