DMOps: Data Management Operation and Recipes

Data-centric AI has shed light on the significance of data within the machine learning (ML) pipeline. Recognizing its significance, academia, industry, and government departments have suggested various NLP data research initiatives. While the ability to utilize existing data is essential, the abilit...

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Bibliographic Details
Main Authors Choi, Eujeong, Park, Chanjun
Format Journal Article
LanguageEnglish
Published 02.01.2023
Subjects
Computer Science - Databases
Computer Science - Learning
Statistics - Methodology
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Summary:Data-centric AI has shed light on the significance of data within the machine learning (ML) pipeline. Recognizing its significance, academia, industry, and government departments have suggested various NLP data research initiatives. While the ability to utilize existing data is essential, the ability to build a dataset has become more critical than ever, especially in the industry. In consideration of this trend, we propose a "Data Management Operations and Recipes" to guide the industry in optimizing the building of datasets for NLP products. This paper presents the concept of DMOps which is derived from real-world experiences with NLP data management and aims to streamline data operations by offering a baseline.
DOI:10.48550/arxiv.2301.01228