Hyperparameter tuning optimizes the settings that control how an AI model learns — things like learning rate, batch size, number of layers, and regularization strength. These settings are chosen before training begins and significantly affect how well the resulting model performs.
The difference between a well-tuned and poorly tuned model on the same data and architecture can be substantial. Tuning them systematically — rather than guessing — produces models that are more accurate, more efficient, and more reliable in production. It is one of the more technical but genuinely impactful steps in the model development process.
