Abstract: Supplementing artificial intelligence (AI)/machine learning (ML) models via an action center, providing AI/ML model retraining hardware control, and providing AI/ML model settings management are disclosed. AI/ML models may be deployed on hosting infrastructure where the AI/ML models can be called by robotic process automation (RPA) robots. When the performance of an AI/ML model falls below a threshold, the result of the AI/ML model prediction and other data is sent to an action center where a human reviews the data using a suitable application and approves the prediction or provides a correction if the prediction is wrong. This action center-approved result is then sent to the RPA robot to be used instead of the prediction from the AI/ML model.

Abstract: Some embodiments address unique challenges of provisioning RPA software to airgapped hosts, and in particular, provisioning RPA machine learning components and training corpora of substantial size, and provisioning to multiple airgapped hosts having distinct hardware and/or software specifications. To reduce costs associated with data traffic and manipulation, some embodiments bundle together multiple RPA components and/or training corpora into an aggregate package comprising a deduplicated collection of software libraries. Individual RPA components are then automatically reconstructed from the aggregate package and distributed to airgapped hosts.

Abstract: A centralized platform for validation of machine learning (ML) models for robotic process automation (RPA) before deployment is provided. The validation platform may support multiple programming languages and build platforms in a single centralized platform. The platform may allow the user to upload the model in a predefined package structure. The platform may then validate the package for deployment.

Abstract: Some embodiments address unique challenges of provisioning RPA software to airgapped hosts, and in particular, provisioning RPA machine learning components and training corpora of substantial size, and provisioning to multiple airgapped hosts having distinct hardware and/or software specifications. To reduce costs associated with data traffic and manipulation, some embodiments bundle together multiple RPA components and/or training corpora into an aggregate package comprising a deduplicated collection of software libraries. Individual RPA components are then automatically reconstructed from the aggregate package and distributed to airgapped hosts.

Abstract: Supplementing artificial intelligence (AI)/machine learning (ML) models via an action center, providing AI/ML model retraining hardware control, and providing AI/ML model settings management are disclosed. AI/ML models may be deployed on hosting infrastructure where the AI/ML models can be called by robotic process automation (RPA) robots. When the performance of an AI/ML model falls below a threshold, the result of the AI/ML model prediction and other data is sent to an action center where a human reviews the data using a suitable application and approves the prediction or provides a correction if the prediction is wrong. This action center-approved result is then sent to the RPA robot to be used instead of the prediction from the AI/ML model.

Abstract: A centralized platform for validation of machine learning (ML) models for robotic process automation (RPA) before deployment is provided. The validation platform may support multiple programming languages and build platforms in a single centralized platform. The platform may allow the user to upload the model in a predefined package structure. The platform may then validate the package for deployment.