- Release Notes
- Getting started
- Notifications
- Projects
- Datasets
- Data Labeling
- ML packages
- Out of the box packages
- Pipelines
- ML Skills
- About ML Skills
- Managing ML Skills
- ML Logs
- Document UnderstandingTM in AI Center
- AI Center API
- Licensing
- AI Solutions Templates
- How to
- Basic Troubleshooting Guide
About ML Skills
An ML skill is a consumer-ready, live deployment of an ML or Open Source Package. Once deployed as ML skills, both ML Packages and Open Source packages become models ready to be consumed within RPA workflows, simply by dragging and dropping an ML Skill Activity in UiPath® Studio.
- The name of the ML Skill
- The name of the package
- The version of the ML Skill
- The status of the ML Skill
- The time the ML Skill was deployed
- If GPU is required
- The number of predictions made
You can deploy packages by creating ML skills, delete them, view their detailed information, parameters, versions, or manage package versions - if available.
The ML Skill Details page, accessible by selecting a skill from the list, contains detailed information about a skill. You can check the current Infra Settings hardware deployment (Replica Count, Resources Per Replica) along with the hosting cost for this ML Skill (AI Units (hourly).
Find information about how to manage your skills here.
To use a skill in a UiPath Studio Activity, the following environment must be set up:
- UiPath Studio v2019.10+, with UiPath Robot v2019.10+
- Installed UiPath.MLServices.Activities package from the Manage Packages menu in UiPath Studio.
Open UiPath Studio, drag and drop the ML Skill Activity into the RPA workflow and select the Refresh ML Skills option. This action will populate the drop-down list with all the successfully deployed skills from the Orchestrator connected to this Robot.
Pass the data to the input of the skill exactly as you would with any other activity. The skill also allows you to live-test an input by selecting Test Skill.
Watch the following video to learn how to use the Test Skill button to see what the deployed model is doing:
Depending on the input type, the MLSkills activity expects the following format:
JSON
"this is an example of input"
"{""expected-field"":""this is another example""}""this is an example of input"
"{""expected-field"":""this is another example""}"File
"C:/full/path/to/file.ext""C:/full/path/to/file.ext"Files
"C:/full/path/to/file1.ext,C:/full/path/to/file2.ext,C:/full/path/to/file3.ext""C:/full/path/to/file1.ext,C:/full/path/to/file2.ext,C:/full/path/to/file3.ext"
