Ajuste preciso

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

A medida que tu flujo de trabajo RPA procesa los documentos usando un modelo ML existente, algunos documentos pueden requerir la validación humana con la actividad Mostrar estación de validación (disponible en los robots asistidos o en el navegador usado Action Center de Orchestrator).

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.

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 see the Import Documents section of the Data Manager documentation.

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

Importante:

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.