Dell Products Inc.

Sashibhusan Panda

Hung The Dinh

Shishir Kumar Parhi

Seshadri Srinivasan

Sweta Kumari

Sambasivarao Gaddam

Baishali Roy

Bijan Kumar Mohanty

Antarlina Tripathy

Shivangi Geetanjali

Shivangi Maharana

Harikrishna Reyyi

Sowmya Kumar

Panguluru Vijaya Sekhar

Kiran Kumar Pidugu

Sourav Datta

Satish Ranjan Das

Krishna Mohan Akkinapalli

Lakshman Kumar Tiwari

Yash Khare

Sowmya K

Bharath Alla

Shashikiran Rajagopal

Venkata Nagendra Purushotham Musti

A method comprises collecting data corresponding to a plurality of components in a system, wherein the data comprises information about one or more issues with the plurality of components. The data is analyzed and categorized based at least in part on the analysis. In the method, one or more application programming interfaces (APIs) are selected to monitor respective statuses of the plurality of components, wherein the selection is based at least in part on the categorization of the data, and the data is pushed to the one or more APIs.

Component monitoring framework

A method comprises collecting data corresponding to one or more code changes in response to committing of the one or more code changes to a code repository, and formatting the data into at least one data string. The at least one data string is inputted to one or machine learning models. Using the one or machine learning models, a natural language description of the one or more code changes is generated based at least in part on the at least one data string. The method further comprises causing transmission of the natural language description of the one or more code changes to a document repository.

Code modification management using machine learning

A method comprises collecting data corresponding to a plurality of components in a system, wherein the data comprises information about at least one of respective protocols and respective interfaces associated with respective ones of the plurality of components. The data is analyzed to determine at least one of the respective protocols and the respective interfaces associated with the respective ones of the plurality of components. In the method, operations of one or more components of the plurality of components are tested based at least in part on the determination of the at least one of the respective protocols and the respective interfaces. The method further includes outputting respective statuses of the one or more components, wherein the respective statuses are derived at least in part from the testing.

Testing framework with load forecasting

A method comprises receiving a request for validation of software comprising one or more applications, analyzing the request and generating one or more validation steps based at least in part on the analysis. In the method, a time to complete the one or more validation steps is predicted. The predicting is performed using one or more machine learning models, and is based at least in part on a type and a number of the one or more applications.

Software validation framework

Component monitoring framework with predictive analytics

A method comprises managing multiple tasks of multiple entities associated with a deployment of a software program with a deployment framework comprising a machine learning module configured to assist with managing the multiple tasks of the multiple entities. The managing step comprises tracking a status of one or more of the multiple tasks, and predicting a time taken for a given one of the multiple entities to complete a given one of the multiple tasks.

Enterprise deployment framework with artificial intelligence/machine learning

Growing community of inventors

Ganjam, India

Sashibhusan Panda