AI Center is the infrastructure on top of which UiPath Document Understanding machine learning models run. These models can be deployed or instantiated for retraining with a few clicks (see this section).
Using a Document Understanding model involves these steps:
- Collect document samples and the requirements of the data points that need to be extracted.
- Label documents using Data Manager.
Data Manager itself will connect to an OCR Engine.
- Export labeled documents as a Training data set and upload that exported folder to AI Center Storage.
- Export labeled documents as a Testing data set and upload that exported folder to AI Center Storage.
- Run a Training Pipeline on AI Center.
- Evaluate the model performance with an Evaluation Pipeline on AI Center.
- Deploy the trained model as an ML Skill in AI Center.
- Query the ML Skill from an RPA Workflow using the Document Understanding ML activity pack.
The diagrams below show how the various components of Document Understanding communicate together for different environments. The diagram also shows a short description of artifacts that are needed for the installation. Each section describes the license artifacts in detail.
The diagrams show UiPath OCR however, there are various other OCR Engines that can be used with the Document Understanding framework.
Data Manager as a part of AI Fabric is in private preview. This preview can be accessed in the insider portal. In this setup, the components are simplified as below:
Updated 23 days ago