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2021

Data Engineering for Everyone

Reddi, Vijay Janapa, Diamos, Greg, Warden, Pete et al.

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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.

Data Engineering for Everyone · Around

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M. D. Wilkinson, M. Dumontier, I. J. Aalbersberg, G. Appleton, M. Axton, A. Baak, N. Blomberg, J.-W. Boiten, L. B. da Silva Santos, P. E. Bourne, et al
2016
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“Facebook AI Research.” https://research.fb.com/publications/
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“Google AI Research.” https://research.google/pubs/
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“MovieLens 25M Dataset.” https://grouplens.org/datasets/movielens/25m/
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“DeepSpeech.” https://github.com/mozilla/DeepSpeech
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“CheXpert.” https://stanfordmlgroup.github.io/competitions/chexpert/
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“MLCommons.” https://mlcommons.org/en/
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“MLCommons People’s Speech.” https://mlcommons.org/en/peoples-speech/
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Then

M. McAuliffe, M. Socolof, S. Mihuc, M. Wagner, and M. Sonderegger, “Montreal forced aligner [computer program],” 2017
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