👉 Instance engineering is a subset of machine learning that focuses on creating and optimizing individual training instances from a larger dataset, typically to improve the performance of machine learning models in specific scenarios or tasks. It involves techniques such as data augmentation, synthetic data generation, and feature engineering to enhance the quality and diversity of training examples. By creating these tailored instances, instance engineering helps models generalize better to unseen data and perform more effectively in real-world applications, especially when labeled data is scarce or expensive to obtain. This approach bridges the gap between raw data and the high-quality inputs required for robust machine learning models, ultimately leading to improved model accuracy and efficiency.
