research-article

Data Lifecycle Challenges in Production Machine Learning: A Survey

Authors:

Neoklis Polyzotis,

Steven Euijong Whang,

Martin ZinkevichAuthors Info & Claims

ACM SIGMOD Record, Volume 47, Issue 2

Pages 17 - 28

https://doi.org/10.1145/3299887.3299891

Published: 11 December 2018 Publication History

Abstract

Machine learning has become an essential tool for gleaning knowledge from data and tackling a diverse set of computationally hard tasks. However, the accuracy of a machine learned model is deeply tied to the data that it is trained on. Designing and building robust processes and tools that make it easier to analyze, validate, and transform data that is fed into large-scale machine learning systems poses data management challenges. Drawn from our experience in developing data-centric infrastructure for a production machine learning platform at Google, we summarize some of the interesting research challenges that we encountered, and survey some of the relevant literature from the data management and machine learning communities. Specifically, we explore challenges in three main areas of focus - data understanding, data validation and cleaning, and data preparation. In each of these areas, we try to explore how different constraints are imposed on the solutions depending on where in the lifecycle of a model the problems are encountered and who encounters them.

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Data Lifecycle Challenges in Production Machine Learning: A Survey
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  1. Machine learning

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Information & Contributors

Information

Published In

cover image ACM SIGMOD Record

ACM SIGMOD Record Volume 47, Issue 2

June 2018

68 pages

ISSN:0163-5808

DOI:10.1145/3299887

Editors:
Yanlei Diao
University of Massachusetts Amherst
,
Vanessa Braganholo
Universidade Federal Fluminense
,
Marco Brambilla
Politecnico di Milano
,
Chee Yong Chan
National University of Singapore
,
Rada Chirkova
North Carolina State University
,
Zackary Ives
University of Pennsylvania
,
Anastasios Kementsietsidis
Google Research
,
Jeffrey Naughton
University of Wisconsin-Madison
,
Frank Neven
Hasselt University
,
Olga Papaemmanoui
Brandeis Univesity
,
Aditya Parameswaran
University of Illinois
,
Alkis Simitsis
HP Labs
,
Wang-Chiew Tan
University of California Santa Cruz
,
Pinar Tözü
IBM Almaden Research Center
,
Marianne Winslett
University of Illinois
,
Jun Yang
Duke University

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Published: 11 December 2018

Published in SIGMOD Volume 47, Issue 2

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Husom ESen SGoknil ABosch JLewis GCleland-Huang JMuccini H(2024)Engineering Carbon Emission-aware Machine Learning PipelinesProceedings of the IEEE/ACM 3rd International Conference on AI Engineering - Software Engineering for AI10.1145/3644815.3644943(118-128)Online publication date: 14-Apr-2024
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