Google's TensorFlow team released TensorFlow Lattice today to help developers ensure that their machine learning models adhere to global trends even when training data is noisy. Lattice draws from the concept of lookup tables to simplify the process of defining macro rules to restrict models. A lookup table is a representation of data that includes inputs (keys) and outputs (values). It's easiest to conceptualize with a single key linking to a single output, but there can be multiple keys in the case of more complex multi-dimensional functions. Roughly speaking, the TensorFlow team's approach is to train the lookup table values using training data to maximize accuracy given constraints.