👉 Instances engineering is a specialized subset of machine learning focused on managing and optimizing the use of individual data instances within a larger model or system. It involves techniques to ensure that each instance is utilized effectively and efficiently, such as by maintaining diverse and representative datasets, handling class imbalance, and ensuring that instances are appropriately weighted or sampled. This process is crucial in scenarios where the quality and representativeness of individual data points significantly impact model performance, such as in personalized recommendation systems or medical diagnostics. Instances engineering also addresses challenges like data privacy and bias by carefully curating and anonymizing instances, thereby enhancing model robustness and fairness.
