Iterate Studio, Inc.

John Selvadurai

Brainerd Sathianathan

Arulkumaran Chandrasekaran

Chatura Samarasinghe

Shomron Jacob

Frederick Michael Frazzini

Jon C Nordmark

David Ruvalcaba Gonzalez

A configuration server is provided including one or more processors and a workflow generator executing on the one or more processors. The workflow generator is configured to generate a workflow corresponding to a feature of a service. The configuration server also includes a client user interface configured to receive requests from a client user to enable or disable the feature of the service. A service director is configured to control deployment of the feature of the service by placing the workflow corresponding to the feature in an active state or an inactive state responsive to client requests. Responsive to a determination that the workflow is in the active state, the service director communicates the workflow to a deployment server. The feature associated with the workflow is available to an end user accessing the service when the workflow is communicated to the deployment server.

Dynamic feature loading

A method of comparing performance of a first entity and a second entity uses performance indexing based on comparable performance metrics. A first entity performance rate and a first growth ability for the first entity are determined based on a first plurality of entity characteristics for the first entity. The first performance rate and the first growth ability are weighted based of the first plurality of entity characteristics and a first entity index value is determined based on the weighted first performance rate and first growth ability. A comparison between the first entity and the second entity is generated using the first entity index value and a second entity index value for the second entity.

Indexing entities based on performance metrics

The present disclosure relates generally to systems and methods that include a configuration server with a processing element operable to operate a plurality of containers. A plurality of functionality modules are operable to execute within respective containers of the plurality of containers. Each functionality module includes: an input, a functionality operable to be executed by the processing element to perform an artificial intelligence operation using the input, and an output produced by the functionality. The modular system includes a messaging object operable to connect the output of a first one of the plurality of functionality modules to the input of a second of the plurality of functionality modules. The modular system configures the messaging object to adapt the output of first functionality module to the input of the second functionality module.

Container architecture for modular machine learning

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San Jose, CA, United States of America

John Selvadurai