Abstract: In some aspects, the techniques described herein relate to a method including: receiving an input text prompt at a machine learning model, the input text prompt including a type of application and a data; mapping, by a text encoder, the input text prompt to a representation space; generating, by a small language model, contextual embeddings for the type of application and the data specified in the input text prompt, wherein a character of each word of the input text prompt encoded with a numerical value; storing, by a computer program, the contextual embeddings in a model repository; assigning, by the computer program, one or more unique tags to the contextual embeddings; generating, by the computer program, a semantic score match based on a data model; generating a visualization of the data model; and generating a script to be executed on a target system for the data model.

Abstract: Systems and methods for generalized structured data discovery utilizing contextual metadata disambiguation via machine learning are disclosed.

Abstract: In one example, a method for updating data models includes selecting a data model standard with which the data model is to comply based on attributes identified therein. The method further includes identifying a set of attributes required by the standard, but not present in the data model, and identifying at least one highest priority change to apply to the data model to increase compliance of the data model with the standard, based on weightings of priority of the set of attributes. The method further includes visually presenting a request for approval to apply the at least one highest priority change to the data model, and applying the at least one highest priority change to the data model and updating at least one of the weightings of priority based on whether the response comprises approval to apply the at least one highest priority change.