Publish a model as a service in the Data Science Workspace UI
NOTEAdobe Experience Platform Data Science Workspace allows you to publish your trained and evaluated Model as a Service, enabling users within your organization to score data without the need for creating their own Models.
Getting started
In order to complete this tutorial, you must have access to Experience Platform. If you do not have access to an organization in Experience Platform, please speak to your system administrator before proceeding.
This tutorial requires an existing Model with a successful training run. If you do not have a publishable Model, follow the Train and evaluate a Model in the UI tutorial before continuing.
If you prefer to publish a Model by using Sensei Machine Learning APIs, refer to the API tutorial.
Publish a Model
In Adobe Experience Platform, select Models located in the left navigation column, then select the Browse tab to list all existing Models. Select the name of the Model you wish to be published as a Service.
Select Publish near the top right of the Model overview page to start a Service creation process.
Input a desired name for the Service and optionally provide a Service description, select Next when finished.
All successful training runs for to the Model are listed. The new Service will inherit training and scoring configurations from the selected training run.
Select Finish to create the Service and redirect to the Service Gallery to show all available Services, including the newly created Service.
Score using a Service
In Adobe Experience Platform, select the Services tab located in the left navigation column to access the Service Gallery. Find the Service that you wish to use and select Open.
Within the service overview page, select Score.
Select an appropriate input dataset for the scoring run, then select Next. You are asked to do the same step for the scoring dataset. Once you have selected the input and output dataset, you can update the configurations.
When a Service is created, it inherits default scoring configurations. You can review these configurations and adjust them as needed by double-clicking on the values. Once you are satisfied with the configurations, select Finish to begin the scoring run.
On the Service’s Overview page, details of the new scoring job and its progress is shown. Once the job completes, the Most Recent header within the Scoring container is updated.
Next steps
By following this tutorial, you have successfully published a Model as an accessible Service, and scored data using the new Service through the Service Gallery. Continue to the next tutorial to learn how you can schedule automated training and scoring runs on a Service.
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