What does fine-tuning involve in the context of AI models?

Fine-tuning in the context of AI models refers to the process of making adjustments to an already trained model. This typically occurs after the model has undergone initial training on a large dataset, where it learns general patterns and features. Fine-tuning involves updating the weights of the model using a smaller, possibly more specific dataset, enabling it to adapt to particular tasks or domains more effectively.

This approach is advantageous because it leverages the existing knowledge within the model, allowing for faster convergence and often improved performance on specialized tasks compared to training a new model from scratch. Fine-tuning can help improve accuracy and relevance when deploying models in specific contexts, making it a common practice in AI and machine learning applications.