Gold Sentinel is exploring the integration of artificial intelligence to enhance the efficiency and accuracy of its sensor-based systems. The primary challenge is to identify the most suitable AI model that can process data from hundreds of sensors in real-time. The project aims to evaluate various AI models to determine which one offers the best performance in terms of speed, accuracy, and resource efficiency. Learners will be tasked with testing different AI models, analyzing their performance metrics, and recommending the most effective model for real-time applications. This project provides an opportunity for students to apply their knowledge of AI and machine learning, focusing on model evaluation and optimization in a practical, industry-relevant context.

The deliverables for this project include a comprehensive report detailing the evaluation process, performance metrics, and a recommendation of the best AI model for Gold Sentinel's needs. Additionally, a presentation summarizing the findings and justifying the chosen model will be required. The team will also provide a demonstration of the selected AI model running with simulated sensor data to showcase its real-time processing capabilities.