This topic describes how to manage the models that have completed training jobs in the AI development console. You can also evaluate and deploy trained models in the AI development console.
- A professional Kubernetes cluster of Kubernetes 1.20 or later is created. For more information, see Create a professional managed Kubernetes cluster.
- The AI development console and scheduling component are installed. For more information, see Deploy the cloud-native AI component set.
- A Resource Access Management (RAM) user is created in the RAM console and attached with a quota group. For more information about how to create a RAM user, see Create a RAM user. For more information about how to attach a quota group to a RAM user, see Step 1: Add a quota group and associate the quota group with the RAM user.
- A training job is completed. For more information, see Submit TensorFlow jobs and CronJobs.
- Log on to the AI development console. For more information, see Step 2: Log on to the AI development console.
- In the left-side navigation pane of the AI development console, click Model Manage.
- On the Model Manage page, click Create Model.
- In the Create dialog box, set Model Name, Model Version, and Job Name. In this example, the model name is set to test-model, the model version is set to 1, and the tf-dist-git job is selected.
- Click OK. After the model is created, you can view basic information about the model in the Model Manage List section.