Bayesian optimization is a probabilistic approach to optimize hyperparameters by modeling the objective function as a probability distribution. It intelligently explores the hyperparameter space to find optimal configurations with minimal trials.

NYD Application: Used to efficiently tune our AI models for maximum performance on NYD-specific tasks without exhaustive grid search.

Example: "Bayesian optimization helped us find the best model configuration for our code quality assessment tool in just 50 trials instead of thousands."