All Products
Document Center

Platform For AI:Periodically update online model services

Last Updated:Mar 06, 2024

Machine Learning Designer provides the Update EAS Service (Beta) component to periodically update model services.


A model generated by Machine Learning Designer is deployed to Elastic Algorithm Service (EAS) as an online service and runs as expected. For more information, see Deploy a model as an online service.

Configure and run the component

The input port of the Update EAS Service (Beta) component can be connected to the path of a model that is stored in an Object Storage Service (OSS) bucket. For example, you can use Predictive Model Markup Language (PMML) models generated by machine learning algorithms or models generated by vision, text processing, and XGBoost training algorithms. You can perform the following steps to configure the Update EAS Service (Beta) component:

  1. Drag the Update EAS Service (Beta) component to the canvas and connect this component as a downstream node to a component that generates a model. Make sure that the model output port of the upstream component is directly connected to the input port of the Update EAS Service component.image

  2. Click the Update EAS Service (Beta) component. In the panel that appears, configure the parameters on the Parameters Settings tab.image

    • EAS Service Name: the name of the deployed EAS service. The service is in the Running state.

    • EAS service description json: the JSON file that describes the service. In most cases, you can leave this parameter empty. If you want to modify parameters in the file, modify the parameters and enter the content that you modify in the code editor. For more information about how to modify the parameters, see Deploy model services by using EASCMD or DSW.

  3. Right-click the Update EAS Service (Beta) component and select Run Current Node.

Periodically update online model services

If you want the previous pipeline that implements model training and service updates to be periodically run, submit the pipeline to DataWorks and schedule it as a periodic task. For more information, see Use DataWorks tasks to schedule pipelines in Machine Learning Designer.


You can go to the EAS-Online Model Services page to view the statuses of deployed services or manage the services. For more information, see Manage online model services in EAS.