  Automatic machine learning (\AML) is a family of techniques to automate the
process of training predictive models, aiming to both improve performance and
make machine learning more accessible. While many recent works have focused on
aspects of the machine learning pipeline like model selection, hyperparameter
tuning, and feature selection, relatively few works have focused on automatic
data augmentation. Automatic data augmentation involves finding new features
relevant to the user's predictive task with minimal ``human-in-the-loop''
involvement.
  We present \system, an end-to-end system that takes as input a dataset and a
data repository, and outputs an augmented data set such that training a
predictive model on this augmented dataset results in improved performance. Our
system has two distinct components: (1) a framework to search and join data
with the input data, based on various attributes of the input, and (2) an
efficient feature selection algorithm that prunes out noisy or irrelevant
features from the resulting join. We perform an extensive empirical evaluation
of different system components and benchmark our feature selection algorithm on
real-world datasets.
