  AutoML systems build machine learning models automatically by performing a
search over valid data transformations and learners, along with hyper-parameter
optimization for each learner. Many AutoML systems use meta-learning to guide
search for optimal pipelines. In this work, we present a novel meta-learning
system called KGpip which, (1) builds a database of datasets and corresponding
pipelines by mining thousands of scripts with program analysis, (2) uses
dataset embeddings to find similar datasets in the database based on its
content instead of metadata-based features, (3) models AutoML pipeline creation
as a graph generation problem, to succinctly characterize the diverse pipelines
seen for a single dataset. KGpip's meta-learning is a sub-component for AutoML
systems. We demonstrate this by integrating KGpip with two AutoML systems. Our
comprehensive evaluation using 126 datasets, including those used by the
state-of-the-art systems, shows that KGpip significantly outperforms these
systems.
