Project Overview

This research presents AI/ML Bench Guard, a comprehensive benchmarking framework for evaluating cloud-based, LLM, and open-source machine learning services. The system conducts automated performance assessments across multiple providers, including AWS, Azure, GCP, and open-source alternatives, focusing on object detection, sentiment analysis, facial recognition, and activity recognition tasks.

By implementing standardized testing protocols and continuous monitoring, AI/ML Bench Guard enables objective comparison of service performance, reliability, and cost-effectiveness while analyzing potential biases in model outputs. The framework features a public-facing dashboard for real-time performance visualization and historical trend analysis, promoting transparency in AI service evaluation.

Results demonstrate significant improvements in service provider selection efficiency, operational cost reduction, and system reliability. This research contributes to the field by providing a standardized methodology for evaluating AI services and fostering trust through transparent performance metrics.