The process of determining the right combination of hyperparameters that allows the machine learning model to maximize its performance.
Unlike model weights, which are learned during training, hyperparameters (such as learning rate, batch size, and network depth) are set beforehand. Hyperparameter Tuning searches for the configuration that yields the most accurate and efficient Private AI model, often through grid searches that are computationally expensive.
See also Private AI, Large Language Model, Fine-Tuning and Model Inference.