A Data-Centric Online Market for Machine Learning: From Discovery to
  Pricing
Minbiao Han Jonathan Light Steven Xia Sainyam Galhotra Raul Castro Fernandez Haifeng Xu
Data fuels machine learning (ML) - rich and high-quality training data is
essential to the success of ML. However, to transform ML from the race among a
few large corporations to an accessible technology that serves numerous normal
users' data analysis requests, there still exist important challenges. One gap
we observed is that many ML users can benefit from new data that other data
owners possess, whereas these data owners sit on piles of data without knowing
who can benefit from it. This gap creates the opportunity for building an
online market that can automatically connect supply with demand. While online
matching markets are prevalent (e.g., ride-hailing systems), designing a
data-centric market for ML exhibits many unprecedented challenges.
  This paper develops new techniques to tackle two core challenges in designing
such a market: (a) to efficiently match demand with supply, we design an
algorithm to automatically discover useful data for any ML task from a pool of
thousands of datasets, achieving high-quality matching between ML models and
data; (b) to encourage market participation of ML users without much ML
expertise, we design a new pricing mechanism for selling data-augmented ML
models. Furthermore, our market is designed to be API-compatible with existing
online ML markets like Vertex AI and Sagemaker, making it easy to use while
providing better results due to joint data and model search. We envision that
the synergy of our data and model discovery algorithm and pricing mechanism
will be an important step towards building a new data-centric online market
that serves ML users effectively.