When choosing between two models of comparable accuracy and computational performance, there are several factors to consider. Some of the key factors that you should consider when choosing a model for production include:
Robustness and generalization: The model should be robust and generalize well to new data, in order to avoid overfitting and to maintain good performance on unseen data.
Explainability and interpretability: The model should be explainable and interpretable, in order to understand how it makes predictions and to ensure that it is making decisions that are aligned with the goals and values of the organization.
Scalability and ease of deployment: The model should be scalable and easy to deploy, in order to support high-volume workloads and to integrate seamlessly into the organization's existing infrastructure and processes.
Data and privacy considerations: The model should be designed and trained in accordance with the organization's data and privacy policies and standards, in order to protect sensitive information and to ensure compliance with relevant regulations.
Overall, when choosing between two models of comparable accuracy and computational performance, you should consider factors such as robustness, explainability, scalability, and data and privacy considerations, in order to select the model that is best suited for production.