# Data Virtualization for Machine Learning

arXiv: https://arxiv.org/abs/2507.17293 · HTML: https://mlsanity.com/paper/2507.17293/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 15 references that are in the map.
- First posted on arXiv 23 Jul 2025.

## Builds on (most cited first, 1 of 1)

- [Version 0.1 of the BigDAWG Polystore System](https://mlsanity.com/paper/1707.00721/) (2017)

## Abstract (arXiv)

Nowadays, machine learning (ML) teams have multiple concurrent ML workflows for different applications. Each workflow typically involves many experiments, iterations, and collaborative activities and commonly takes months and sometimes years from initial data wrangling to model deployment. Organizationally, there is a large amount of intermediate data to be stored, processed, and maintained. Data virtualization becomes a critical technology in an infrastructure to serve ML workflows. In this paper, we present the design and implementation of a data virtualization service, focusing on its service architecture and service operations. The infrastructure currently supports six ML applications, each with more than one ML workflow. The data virtualization service allows the number of applications and workflows to grow in the coming years.