Add a workflow for a custom model
After you create a custom model, the system adds it as a workflow in Customer Insights - Data. The workflow sends data from Customer Insights - Data to your Azure Machine Learning model and then returns the results into Customer Insights - Data. After you configure the workflow and start using the model, you can use the model to create items, such as customer segments, that other applications can use.
To create custom models, go to Intelligence, select Customer Models, and then select the New Workflow button.
The process to configure a workflow consists of three steps:
- Provide workflow details.
- Select the data to send.
- Relate the results to customer data.
The first step is to define the necessary workflow details. The three items that you need to define are:
- Name - Defines the name of the object in Customer Insights - Data.
- Tenant - Specifies the Azure Machine Learning tenant where the web service that contains your model is deployed.
- Web service - Specifies the web service that your model is deployed to.
For the machine learning model to provide you with results, Customer Insights - Data needs to know what data to send to the model as inputs. The number of inputs varies depending on the model that you're working with. For each web service input that's listed, you need to supply a matching table. The following example shows that the web service needs three inputs.
Important
A table is only present in Customer Insights - Data if it's ingested into the application. Before you create the workflow, make sure that any datasets that you intend to use as inputs are ingested into the application.predict-subscription-churn.md
The last step in the process is to select the field in your customer table that represents the Customer ID. The application uses this field to match the results to the appropriate customer in the application.
