  Machine Learning models are increasingly being adopted in many applications.
The quality of these models critically depends on the input data on which they
are trained, and by augmenting their input data with external data, we have the
opportunity to create better models. However, the massive number of datasets
available on the Web makes it challenging to find data suitable for
augmentation. In this demo, we present our ongoing efforts to develop a dataset
search engine tailored for data augmentation. Our prototype, named Auctus,
automatically discovers datasets on the Web and, different from existing
dataset search engines, infers consistent metadata for indexing and supports
join and union search queries. Auctus is already being used in a real
deployment environment to improve the performance of ML models. The
demonstration will include various real-world data augmentation examples and
visitors will be able to interact with the system.
