@article {1595404,
	title = {Data Engineering for Everyone},
	year = {2021},
	abstract = {Data engineering is one of the fastest-growing fields within machine learning (ML). As ML becomes more common, the appetite for data grows more ravenous. But ML requires more data than individual teams of data engineers can readily produce, which presents a severe challenge to ML deployment at scale. Much like the software-engineering revolution, where mass adoption of open-source software replaced the closed, in-house development model for infrastructure code, there is a growing need to enable rapid development and open contribution to massive machine learning data sets. This article shows that open-source data sets are the rocket fuel for research and innovation at even some of the largest AI organizations. Our analysis of nearly 2000 research publications from Facebook, Google and Microsoft over the past five years shows the widespread use and adoption of open data sets. Open data sets that are easily accessible to the public are vital to accelerate ML innovation for everyone. But such open resources are scarce in the wild. So, can we accelerate data set creation and enable the rapid development of open data sets, akin to the rapid development of open-source software? Moreover, can we develop automatic data set generation frameowrks and tools to avert the data scarcity crisis?},
	url = {https://arxiv.org/abs/2102.11447},
	author = {Reddi, Vijay Janapa and Greg Diamos and Pete Warden and Peter Mattson and David Kanter}
}
