Using proxy models to estimate the impact of changes on expensive ML models like neural networks presents several challenges and limitations:
Accuracy: Proxy models may not capture the full complexity of the original neural network model, leading to inaccuracies in estimating the impact of changes. Differences in model architecture, training data, and hyperparameters can result in discrepancies between the proxy model's predictions and the actual impact on the neural network.
Generalization: Proxy models may struggle to generalize across different types of changes or scenarios, limiting their applicability in estimating the impact of diverse modifications to the neural network. The proxy model's performance may degrade when extrapolating beyond the specific conditions for which it was trained.
Training Data: Acquiring sufficient and representative training data for the proxy model can be challenging, especially when aiming to mimic the behavior of a complex neural network. Biases in the training data or inadequate coverage of the neural network's operational space can compromise the proxy model's effectiveness.
Computational Overhead: Training and maintaining a proxy model alongside the original neural network can introduce additional computational overhead. The resources required for developing and updating the proxy model may offset the benefits gained from estimating the impact of changes on the neural network.
Interpretability: Proxy models may lack interpretability compared to the original neural network, making it challenging to understand the rationale behind their predictions. This lack of transparency can hinder the user's ability to trust the proxy model's estimates of the impact of changes.
Complexity: Managing the interaction between the proxy model and the neural network, especially in scenarios with intricate dependencies and feedback loops, can introduce complexity and potential errors. Ensuring the consistency and coherence of the proxy model's estimations in such complex settings is a significant challenge.
Addressing these challenges and limitations requires careful consideration of the trade-offs involved in using proxy models to estimate the impact of changes on expensive ML models like neural networks. By acknowledging these factors, the system can make informed decisions about when and how to leverage proxy models effectively in the estimation process.