Morgan Stanley Services Group Inc.

Sanjit Vijay Mehta

Mehak Mehta

Rahul Suresh

Mayank Jain

Suryakant Brahmbhatt

Vijay Kumar Butte

Ashish Kumar Singh

Abhijit Anant Naik

Meet Singh

Satya Verma

Sourabh Kumar Janghel

Aditya Ramesh

Lokin Makkena

A system and method may create and manage a code repository structure and related files for a ML system such as a NN by executing code to create a version control management platform template commit including a pipeline folder comprising deploy files; a configuration folder comprising key-value pairs describing variables used as input to the neural network; a notebooks folder; a steps folder including ML or NN code; a tests folder comprising test scripts; and a utils folder; and executing code to create a tag associated with a pull request and alter version control management platform data such that the tag is displayed in conjunction with data describing the branch associated with the pull request. Executing code to create a tag may include searching a commit history for a unique commit number associated with a pull request, and based on this search creating a tag.

System and method for machine learning development

Systems and methods for translating natural language to SQL on a custom enterprise data warehouse powered by Generative AI. With an embodiment of the present invention, a natural language question may be converted to a meaningful and accurate database query, e.g., SQL query, relevant to tables existing in an enterprise data warehouse. An embodiment of the present invention is directed to a comprehensive approach of transforming a natural language query to a focused SQL query using domain specific data models across firmwide metadata systems and data systems. In response to a user query, an embodiment of the present invention performs metadata analysis, targeted data retrieval and then SQL generation. An embodiment of the present invention may apply data warehousing standards and guidelines followed in the enterprise and provide a plug-and-play type architecture and solution that is scalable to large warehousing and other systems.

Natural language to SQL on custom enterprise data warehouse powered by generative artificial intelligence

A computer-implemented method for extracting data from separate databases and loading the data into a central data store without introducing additional security vulnerabilities is disclosed. The method begins by receiving a configuration comprising one or more transformation functions to be applied to one or more data fields. Values of the one or more data fields are extracted from the databases for a plurality of records, the values being previously tagged with metadata indicating at least a datatype for the values. Based at least in part on the configuration and the metadata tagging, a function from the one or more transformation functions is selected for each value. A second database with a central data store is populated with transformed data. Requestors are not permitted to access to pre-transformed data in the first one or more databases while being allowed access to at least some post-transformed data in the central data store.

Information compartmentalizing data store

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Bengaluru, India

Sanjit Vijay Mehta