Fine-tuning

AI Center includes the capability of fine-tuning ML models using data that has been validated by a human using Validation Station.

As your RPA workflow processes documents using an existing ML model, some documents may require human validation using the Present Validation Station activity (available on attended bots or in the browser using Orchestrator Action Center).

The validated data generated in Validation Station can be exported using Machine Learning Extractor Trainer activity, and can be used to fine-tune ML models in AI Center.

We do not recommend training ML models from scratch (i.e. the DocumentUnderstanding ML Package) using data from Validation Station, but only to fine-tune existing ML models (including out-of-the-box models).

For the detailed steps involved in fine-tuning an ML model see the Import Documents section of the Data Manager documentation.

For more details about how to build a dataset for fine-tuning, go here.

Important: It if often wrongly assumed that the way to use Validation Station data is to iteratively retrain the previous model version, so the current batch is used to train package X.1 to obtain X.2. Then the next batch trains on X.2 to obtain X.3 and so on. This is the wrong way to use the product. Each Validation Station batch needs to be imported into the same Data Manager session as the original manually labeled data making a larger dataset, which must be used to train always on the X.0 ML Package version.