  Data is the fuel powering AI and creates tremendous value for many domains.
However, collecting datasets for AI is a time-consuming, expensive, and
complicated endeavor. For practitioners, data investment remains to be a leap
of faith in practice. In this work, we study the data budgeting problem and
formulate it as two sub-problems: predicting (1) what is the saturating
performance if given enough data, and (2) how many data points are needed to
reach near the saturating performance. Different from traditional
dataset-independent methods like PowerLaw, we proposed a learning method to
solve data budgeting problems. To support and systematically evaluate the
learning-based method for data budgeting, we curate a large collection of 383
tabular ML datasets, along with their data vs performance curves. Our empirical
evaluation shows that it is possible to perform data budgeting given a small
pilot study dataset with as few as $50$ data points.
