The project involves training an AI to infer optimal model placement through neural networks, leveraging existing models' placement positions. This endeavor involves extracting key features from the models, which is a challenging task. The idea is to develop a neural network that can predict the ideal placement position for a given model considering various constraints. The approach seems feasible, but it would require a thorough analysis of the models' characteristics and the development of an efficient feature extraction mechanism. By overcoming the feature extraction hurdle, we can potentially train a robust AI that can accurately predict optimal model placement positions.