- Release Notes
- Before you begin
- Getting started
- Projects
- Datasets
- ML packages
- Pipelines
- ML Skills
- About ML Skills
- Managing ML Skills
- ML Logs
- Document Understanding in AI Center
- Licensing
- How To
- Basic Troubleshooting Guide
Managing ML Skills
- In the ML Skills page, click Create New. The Create New ML Skill page is displayed.
- Enter a name for the new skill. The name should contain only characters, numbers, and underscores and should not start with number.
- Select a package from the drop-down.
- Select package major and minor versions.
- (Optionally) Add a description for the new skill.
- Choose whether or not to enable a GPU on the environment running this skill.
- Choose whether or not to enable auto-update. If this is set to
true, the model is automatically updated with the latest retrained version of the model (same ML Package version, but new minor version). - Choose when to undeploy the Skill after a period of inactivity from the drop-down. A Skill is considered as inactive if it is not used at all and there are no predictions or changes on the deployment itself.
- (Optionally) Toggle the Enable Infra Settings if you want to customize the resources and replicas for this specific deployment. If infra settings is enabled, the following
fields can be customized:
- Number of replicas.
- The request and limit sizes for RAM (GB).
- The request and limit sizes for CPU.
Note: Only change these values if you are sure they are the correct ones. - Click Create. The Create New ML Skill page is closed and the ML Skills page is displayed, containing the new skill with Deploying status.
The model is wrapped in UiPath's serving framework and deployed within a namespace on AI Center's Kubernetes cluster that is only accessible by your tenant.
If the deployment is successful, the status of the skill changes from Deploying to Available.
The ML Skill Details page enables you to manage the package version used within the deployed skill. You can update the skill to use a specific package version, or you can perform a rollback to the previously used version.
- In the ML Skill Details page, click ⇅ next to the package version you want to use. The button is disabled for the version that is currently in use, marked as Current. The Update skill window is displayed.
- In the Update skill window, click OK to update the skill to use the selected package version.
In addition to updating a skill to a different version, a deployed skill can be kept at the same version but modified to use or not use a GPU. To do that, select Modify current deployment in the ML Skill Details page > Update skill window, as below:
You have the possibility to make an ML Skill public. Doing so, the ML Skill will be accessible via an endpoint from outside of UiPath environment. This means that you can call it without the need to go through a robot connected to the specific tenant.
To do that, select Modify current deployment in the ML Skill Details page > Update skill window, as below. Note this will redeploy the ML Skill, once the ML Skill is available you will see the corresponding url in the ML Skill Details and a button will help you copy the url.
Streaming logs can be found in the ML Skill details page. If you click Streaming logs, the following information is displayed related to the selected ML Skill:
- Deployer pod logs for the particular deployment.
- ML Skill pod before it is successfully deployed.
- ML Skill pod log after successful deployment, containing all prediction logs.
- In the ML Skills page, click the Delete button next to a deployed or failed skill. A confirmation window is displayed.
- Click Ok to delete the skill. The selected
skill is undeployed and it disappears from the ML Skills page.
Note: Deleting a skill could affect its consumers. However, a skill with the same name can be re-created at any point restoring dependents of this service.
