Programmatic operators in this framework that let users build and manipulate training datasets include labeling functions for labeling unlabeled data, transformation functions for expressing data augmentation strategies, and slicing functions for partitioning and structuring training datasets. These operators allow domain experts to specify ML models via noisy operators over training data, leading to applications that can be built in hours or days rather than months or years.

This programmatic approach also leads to more systematic and error-analysis driven iterations to develop and monitor AI and ML applications for real-world problems.