Ajuste preciso

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).

Los datos validados generados en la estación de validación pueden exportarse utilizando la actividad Entrenador de extractores de aprendizaje automático y pueden utilizarse para afinar modelos ML en AI Center.

No recomendamos entrenar modelos ML desde cero (es decir, el paquete ML DocumentUnderstanding) con datos de la estación de validación, sino solo para ajustar los modelos ML existentes (incluidos los modelos listos para usar).

For the detailed steps involved in fine-tuning an ML model, check the Import Documents section of the Document Manager documentation.

Importante:

It if often wrongly assumed that the way to use Validation Station data is to retrain the previous model version iteratively, 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 Document 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.