Model fine-tuning refers to the process of adapting a pre-trained machine learning model to perform a specific task by continuing its training on a smaller dataset that is specific to a single task. This leverages the general knowledge the model already has and refines it to cater to specific applications or domains.
Fine-tuning is extremely effective for improving model performance without needing to train a new model from scratch. In enterprise setting, fine-tuning allows organizations to align AI models with their unique customer data, terminology, and operational requirements. This makes AI-powered solutions more accurate and relevant.
