SELF-LEARNING, LLM-DRIVEN, EXPLAINABLE, PRIVACY-AWARE, LEARNING AGENT FOR INTELLIGENT MODEL RECOMMENDATIONS (SELF-LLM-XPLAINER)

Abstract

The present disclosure relates to a system and method for model recommendation, more particularly, a self-learning, LLM-driven, explainable, privacy-aware, learning agent for intelligent model recommendations (SELF-LLM-XPLAINER). The present disclosure suggests ingesting a dataset provided by a user via an online interface. Thereafter, the present disclosure suggests profiling the ingested dataset to generate standardized metadata. Subsequently, assessing the standardized metadata and sampled data values to detect Personally Identifiable Information (PII) and to generate a privacy risk map. Upon generating the privacy risk map, the present disclosure suggests identifying a machine learning task corresponding to the dataset based on the standardized metadata and one or more sample values. Further, the present disclosure suggests analyzing the standardized metadata and identifying task metadata to generate a recommendation model. As a result, the present disclosure provides a self-learning, agentic system for intelligent recommendation of machine learning models and pipelines