Which process involves adjusting and improving AI models after initial training?

Model fine-tuning is the process that focuses on refining and enhancing AI models after their initial training phase. Once a model has been trained on a dataset, it may not perform optimally for a specific application, or it may need to adapt to new data or changing conditions. Fine-tuning involves making adjustments to the model's parameters, often using a smaller or more specific dataset that represents the target task more closely. This allows the model to generalize better and improve its performance on the desired application.

In contrast, data archiving refers to the process of storing data for future use and does not directly impact the performance of AI models. Network monitoring relates to the oversight of network performance and traffic, which is not the same as enhancing an AI model. Behavior tracking involves observing and recording the behavior of systems or users over time, but it does not involve the iterative improvement of model parameters or architectures. Therefore, model fine-tuning is the most relevant and accurate process for adjusting AI models post-training.