  AutoML services provide a way for non-expert users to benefit from
high-quality ML models without worrying about model design and deployment, in
exchange for a charge per hour ($21.252 for VertexAI). However, existing AutoML
services are model-centric, in that they are limited to extracting features and
searching for models from initial training data-they are only as effective as
the initial training data quality. With the increasing volume of tabular data
available, there is a huge opportunity for data augmentation. For instance,
vertical augmentation adds predictive features, while horizontal augmentation
adds examples. This augmented training data yields potentially much better
AutoML models at a lower cost. However, existing systems either forgo the
augmentation opportunities that provide poor models, or apply expensive
augmentation searching techniques that drain users' budgets.
  Kitana is a data-centric AutoML system that also searches for new tabular
datasets that can augment the tabular training data with new features and/or
examples. Kitana manages a corpus of datasets, exposes an AutoML interface to
users and searches for augmentation with datasets in the corpus to improve
AutoML performance. To accelerate search, Kitana applies aggressive
pre-computation to train a factorized proxy model and evaluate each candidate
augmentation within 0.1s. Kitana also uses a cost model to limit the time spent
on augmentation search, supports expressive data access controls, and performs
request caching to benefit from past similar requests. Using a corpus of 518
open-source datasets, Kitana produces higher quality models than existing
AutoML systems in orders of magnitude less time. Across different user
requests, Kitana increases the model R2 from 0.16 to 0.66 while reducing the
cost by >100x compared to the naive factorized learning and SOTA data
augmentation search.
