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: Frameworks and techniques for integration of heterogeneous machine learning (ML) models into robotic process automation (RPA) workflows are provided. This may be accomplished via a seamless drag-and-drop interface that allows deployment of ML models into an RPA workflow. Via a framework, these heterogeneous models may be provided by customers, third parties, and/or partners and integrated into the RPA workflow. The framework may provide a straightforward way to deploy machine learning models via a conductor and to manage model versioning and create/retrieve/update/delete (CRUD). The framework may facilitate integration of different models into the RPA workflow through the steps of uploading, validating, publishing, and deploying models.

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: Systems and methods for operating an RPA (robotic process automation) services delivery platform for implementing a plurality of RPA services on premises of a customer are provided. An installer for installing a plurality of RPA services on one or more computing systems located on premises of a customer is generating using the RPA services delivery platform. One or more of the plurality of RPA services installed on the one or more computing systems using the installer are maintained using the RPA services delivery platform.

Abstract: Robotic process automation (RPA) architectures and processes for hosting, monitoring, and retraining ML machine learning (ML) models are disclosed. Retraining is an important part of the ML model lifecycle. The retraining may depend on the type of the ML model and the data on which the ML model will be trained. A secure storage layer may be used to store data from RPA robots for retraining. This retraining may be performed automatically, remotely, and without user involvement.

Abstract: Robotic process automation (RPA) architectures and processes for hosting, monitoring, and retraining ML machine learning (ML) models are disclosed. Retraining is an important part of the ML model lifecycle. The retraining may depend on the type of the ML model and the data on which the ML model will be trained. A secure storage layer may be used to store data from RPA robots for retraining. This retraining may be performed automatically, remotely, and without user involvement.

Abstract: Frameworks and techniques for integration of heterogeneous machine learning (ML) models into robotic process automation (RPA) workflows are provided. This may be accomplished via a seamless drag-and-drop interface that allows deployment of ML models into an RPA workflow. Via a framework, these heterogeneous models may be provided by customers, third parties, and/or partners and integrated into the RPA workflow. The framework may provide a straightforward way to deploy machine learning models via a conductor and to manage model versioning and create/retrieve/update/delete (CRUD). The framework may facilitate integration of different models into the RPA workflow through the steps of uploading, validating, publishing, and deploying models.