👉 Exposure engineering is a critical aspect of machine learning and AI development that focuses on designing and optimizing the process through which models learn from data. It involves strategically selecting, preparing, and presenting data to improve model performance, ensuring that the training process is efficient and effective. This includes tasks such as data augmentation to increase dataset diversity, feature engineering to extract meaningful information, and the careful tuning of learning rates and other hyperparameters. By meticulously engineering exposure, practitioners can enhance model generalization, reduce overfitting, and ultimately build more robust and reliable AI systems.
