mlsanityData to 7 Oct 2026

What can Data-Centric AI Learn from Data and ML Engineering?

arXiv 2112.06439 ↗First posted 13 Dec 2021cs.LG cs.DBcited by 24

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Abstract (arXiv)

Data-centric AI is a new and exciting research topic in the AI community, but many organizations already build and maintain various "data-centric" applications whose goal is to produce high quality data. These range from traditional business data processing applications (e.g., "how much should we charge each of our customers this month?") to production ML systems such as recommendation engines. The fields of data and ML engineering have arisen in recent years to manage these applications, and both include many interesting novel tools and processes. In this paper, we discuss several lessons from data and ML engineering that could be interesting to apply in data-centric AI, based on our experience building data and ML platforms that serve thousands of applications at a range of organizations.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). Builds on 3 of its 18 references that are in the map. First posted on arXiv 13 Dec 2021.

Builds on

3 of 18 references.

References · 3

  1. 2018Model Cards for Model Reportingcited by 1,068
  2. 2016Data Programming: Creating Large Training Sets, Quicklycited by 246
  3. 2020Towards ML Engineering: A Brief History Of TensorFlow Extended (TFX)cited by 3

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Cited by year

Citing papers per year: 2022: 6; 2023: 15; 2024: 2; 2025: 1.

24 papers.

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  1. 2023Data-centric Artificial Intelligence: A Surveycited by 202
  2. 2023Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in low-data regimescited by 52
  3. 2023Data-centric AI: Perspectives and Challengescited by 43
  4. 2023Can You Rely on Your Model Evaluation? Improving Model Evaluation with Synthetic Test Datacited by 31
  5. 2022The Principles of Data-Centric AI (DCAI)cited by 26
  6. 2022Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular datacited by 22
  7. 2023Better, Not Just More: Data-Centric Machine Learning for Earth Observationcited by 19
  8. 2023Dynamic Datasets and Market Environments for Financial Reinforcement Learningcited by 15
  9. 2023Towards Data-centric Graph Machine Learning: Review and Outlookcited by 15
  10. 2022DC-Check: A Data-Centric AI checklist to guide the development of reliable machine learning systemscited by 14
  11. 2022DendroMap: Visual Exploration of Large-Scale Image Datasets for Machine Learning with Treemapscited by 12
  12. 2023DMOps: Data Management Operation and Recipescited by 7
12 more
  1. 2023TRIAGE: Characterizing and auditing training data for improved regressioncited by 6
  2. 2022Modern Views of Machine Learning for Precision Psychiatrycited by 5
  3. 2023Transition Role of Entangled Data in Quantum Machine Learningcited by 5
  4. 2023GPT Self-Supervision for a Better Data Annotatorcited by 5
  5. 2022Data Smells: Categories, Causes and Consequences, and Detection of Suspicious Data in AI-based Systemscited by 4
  6. 2025Minimizing Risk Through Minimizing Model-Data Interaction: A Protocol For Relying on Proxy Tasks When Designing Child Sexual Abuse Imagery Detection Modelscited by 4
  7. 2023Learning to Select Pivotal Samples for Meta Re-weightingcited by 3
  8. 2024Survey and Taxonomy: The Role of Data-Centric AI in Transformer-Based Time Series Forecastingcited by 3
  9. 2023Trust, Accountability, and Autonomy in Knowledge Graph-based AI for Self-determinationcited by 2
  10. 2023Synthetic Alone: Exploring the Dark Side of Synthetic Data for Grammatical Error Correctioncited by 0
  11. 2023Dataset Factory: A Toolchain For Generative Computer Vision Datasetscited by 0
  12. 2024Data Acquisition for Improving Model Fairness using Reinforcement Learningcited by 0