AutoML Framework for Intelligent Model Selection

Modern Artificial Intelligence (AI) applications use specialized models to perform different tasks.However, selecting a single model for all user queries may result in reduced performance when queries vary in intent and complexity.To overcome this challenge, this project proposes an "AutoML Framework for Intelligent Model Selection," a dynamic multi-agent system consisting of five specialized AI agents: Text Model,Embedding Model, Vision Model, Reasoning Model, and Code/Data Model.These agents collaboratively analyze incoming user queries and identify the most suitable model based on the requirements of each query.The framework incorporates an Automated Machine Learning (AutoML) evaluation engine using five algorithms: K-Nearest Neighbors (KNN), Recurrent Neural Networks (RNN), Linear Regression, Logistic Regression, and Naive Bayes.The system evaluates agent suitability using relevant query features and performance metrics, including Receiver Operating Characteristic (ROC) curves and Area Under the Curve (AUC) scores based on labelled evaluation data.The most suitable agent is selected to generate the final response.An interactive frontend dashboard displays agent scores, algorithmic metrics, selection results, and the reason behind the selected model.The proposed framework aims to provide intelligent, adaptive, and transparent model selection for different types of AI queries.