Mckinsey & Company, Inc.

Neema Uthappa

Rares Ioan Almasan

Sriram Venkatesan

Sastry Vsm Durvasula

Sonam Jha

Jaspreet Singh

Sastry V S M Durvasula

Hugo Sarrazin

A R Nandakumar Reddy

Steven F Reed

Srinivasa Rao Maddili

Kunal Chakrabarty

Sayan Chowdhury

Jennifer L Gilbert

Mujen Inanc

Subhajit Basu

Systems and methods for improved data curation and integration are disclosed. An example system includes one or more processors; and a memory storing instructions that cause the system to: receive an initial query, query an external database to obtain a set of keywords, topics, and other queries pertinent to the initial query, and embed articles into a database. The system may also append the keywords, topics, and other queries to the initial query to obtain an external data query and embedded articles to the initial query to obtain an internal data query; and generate (i) an external output by applying a large language model (LLM) to the external data query and (ii) an internal output by applying the LLM to the internal data query. The system may also combine the internal output with the external output to obtain a combined output and cause the combined output to be displayed.

Systems and methods for improved data curation and integration

A method includes collecting current data and architecture state, collecting future data and architecture state; analyzing the current and/or future data and architecture state to generate deployment options; and causing the summary of options to be displayed. A computing system includes a processor and a memory comprising instructions, that when executed, cause the system to collect current data and architecture state, collect future data and architecture state; analyze the current and/or future data and architecture state to generate deployment options; and cause the summary of options to be displayed. A non-transitory computer-readable storage medium includes executable instructions that, when executed by a processor, cause a computer to collect current data and architecture state, collect future data and architecture state; analyze the current and/or future data and architecture state to generate deployment options; and cause the summary of options to be displayed.

Machine learning techniques for environmental discovery, environmental validation, and automated knowledge repository generation

ML methods and systems for cataloging and making recommendations based on domain-specific knowledge are disclosed. An example method includes: cataloging, using knowledge engines, data to develop knowledge repositories for respective domains; obtain current domain state data; obtain future domain state data; analyze, using first ML models, one or more of (i) data from the knowledge repositories, (ii) the first domain state data, and (iii) the second domain state data to identify a recommended set of one or more regulations, standards, policies and/or rules for a desired second domain state; analyze, using second ML models, (i) the recommended set and (ii) a current data and architecture state for a current computing environment to generate a summary of one or more cloud deployment options for migrating a current computing environment to a future computing environment for a future domain state; and cause the summary to be displayed on a computing device.

Machine learning methods and systems for cataloging and making recommendations based on domain-specific knowledge

A method includes receiving a plurality of user use cases; analyzing the use cases using an AI engine to order the use cases; generating an optimized machine learning model; and causing an optimized deployment option to be displayed. A computing system includes a processor; and a memory comprising instructions, that when executed, cause the computing system to: receive a plurality of user use cases; analyze the use cases using an AI engine to order the use cases; generate an optimized machine learning model; and cause an optimized deployment option to be displayed. A non-transitory computer-readable storage medium stores executable instructions that, when executed by a processor, cause a computer to: receive a plurality of user use cases; analyze the use cases using an AI engine to order the use cases; generate an optimized machine learning model; and cause an optimized deployment option to be displayed.

Artificial intelligence-based use case model recommendation methods and systems

A method includes receiving first input, analyzing the first input using a first model, receiving second input, analyzing the second input using a second model; and generating infrastructure-as-code. A computing system includes a processor; and a memory comprising instructions, that when executed, cause the computing system to: receive first input, analyze the first input using a first model, receive second input, analyze the second input using a second model; and generate infrastructure-as-code. A non-transitory computer-readable storage medium storing executable instructions that, when executed by a processor, cause a computer to: receive first input, analyze the first input using a first model, receive second input, analyze the second input using a second model; and generate infrastructure-as-code.

Automated cloud data and technology solution delivery using machine learning and artificial intelligence modeling

Growing community of inventors

Phoenix, AZ, United States of America

Neema Uthappa