What is a common method for optimizing AI models in Azure?

Hyperparameter tuning is a crucial method for optimizing AI models in Azure, as it involves adjusting the parameters that govern the learning process of models. These parameters, which are not updated during training, significantly influence the model’s performance. By experimenting with different values for hyperparameters, such as learning rate, number of hidden layers, and batch size, one can find the optimal configurations that lead to better accuracy and generalization.

This process can significantly enhance the model's ability to improve its predictive performance based on the training data it receives. Azure offers various tools and capabilities, such as the Azure Machine Learning service, that allow for systematic tuning through techniques like grid search, random search, or more advanced optimization algorithms.

Understanding hyperparameter tuning is essential for practitioners aiming to deploy efficient AI models that yield the best results in terms of performance and resource usage. Thus, leveraging this method in Azure can lead to more robust and effective AI solutions.