Vmware, Inc.

Priyank Agarwal

Ritesh Ranjan Jha

Praveen Kumar

Thomas Clyde Hatch

Vaidic Joshi

Suchit Dhakate

Valentina Reutova

Jasmine Ejner

Charles Mcmarrow

Murali Sampangiramaiah

The current document is directed to an infrastructure-as-code ('IaC') cloud-infrastructure-management service or system that automatically generates parameterized cloud templates that represent already deployed cloud-based infrastructure, including virtual networks, virtual machines, load balancers, and connection topologies. The IaC cloud-infrastructure manager provides an infrastructure-discovery service that accesses a cloud-computing facility to obtain information about already deployed cloud infrastructure and that generates a textual description of the deployed infrastructure, which the IaC cloud-infrastructure-manager then transforms into a set of parameterized cloud-infrastructure-specification-and-configuration files, a resource_ids file, and a parameters file that together comprise a parameterized cloud template.

Methods and systems that automatically generate parameterized cloud-infrastructure templates

The present disclosure provides an approach for training a machine learning model by first training the model on a generic dataset and then iteratively training the model on 'easy' domain specific training data before moving on to 'difficult' domain specific training data. Inputs of a domain-specific dataset are run on the generically-trained model to determine which inputs generate an accuracy score above a threshold. The inputs with an accuracy score above a threshold are used to retrain the model, along with the corresponding outputs. The retraining continues until all domain specific dataset has been used to train the model, or until no remaining inputs of the domain specific dataset generate an accuracy score, when run on the model, that is above a threshold.

Enhanced learning with feedback loop for machine reading comprehension models

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Bengaluru, India

Priyank Agarwal