mlsanityData to 14 Oct 2026

A Data Quality-Driven View of MLOps

arXiv 2102.07750 ↗First posted 15 Feb 2021cs.LG cs.DBcited by 31

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Abstract (arXiv)

Developing machine learning models can be seen as a process similar to the one established for traditional software development. A key difference between the two lies in the strong dependency between the quality of a machine learning model and the quality of the data used to train or perform evaluations. In this work, we demonstrate how different aspects of data quality propagate through various stages of machine learning development. By performing a joint analysis of the impact of well-known data quality dimensions and the downstream machine learning process, we show that different components of a typical MLOps pipeline can be efficiently designed, providing both a technical and theoretical perspective.

Key facts. Cited by 31 papers in the map (data through 14 Oct 2026). Builds on 13 of its 54 references that are in the map. First posted on arXiv 15 Feb 2021.

Builds on

13 of 54 references.

References · 13

  1. 2017Snorkel: Rapid Training Data Creation with Weak Supervisioncited by 372
  2. 2015The Ladder: A Reliable Leaderboard for Machine Learning Competitionscited by 69
  3. 2017Generative Adversarial Networks recover features in astrophysical images of galaxies beyond the deconvolution limitcited by 26
  4. 2019Overton: A Data System for Monitoring and Improving Machine-Learned Productscited by 21
  5. 2020Nearest Neighbor Classifiers over Incomplete Information: From Certain Answers to Certain Predictionscited by 20
  6. 2020Online Active Model Selection for Pre-trained Classifierscited by 20
  7. 2018Using transfer learning to detect galaxy mergerscited by 11
  8. 2019Continuous Integration of Machine Learning Models with ease.ml/ci: Towards a Rigorous Yet Practical Treatmentcited by 9
  9. 2018Patient Risk Assessment and Warning Symptom Detection Using Deep Attention-Based Neural Networkscited by 7
  10. 2019CleanML: A Study for Evaluating the Impact of Data Cleaning on ML Classification Taskscited by 7
  11. 2020On Convergence of Nearest Neighbor Classifiers over Feature Transformationscited by 7
  12. 2018Exploring galaxy evolution with generative modelscited by 4
1 more
  1. 2019Sensing Social Media Signals for Cryptocurrency Newscited by 3

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Citing papers per year: 2021: 4; 2022: 11; 2023: 7; 2024: 7; 2025: 2.

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  1. 2022Machine Learning Operations (MLOps): Overview, Definition, and Architecturecited by 128
  2. 2021Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspectivecited by 116
  3. 2022Data-Centric Artificial Intelligencecited by 31
  4. 2022MLOps -- Definitions, Tools and Challengescited by 25
  5. 2022Operationalizing Machine Learning: An Interview Studycited by 25
  6. 2023Reimagining Synthetic Tabular Data Generation through Data-Centric AI: A Comprehensive Benchmarkcited by 23
  7. 2022Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular datacited by 22
  8. 2023MLOps: A Reviewcited by 16
  9. 2021RLOps: Development Life-cycle of Reinforcement Learning Aided Open RANcited by 15
  10. 2022DC-Check: A Data-Centric AI checklist to guide the development of reliable machine learning systemscited by 14
  11. 2023The Pipeline for the Continuous Development of Artificial Intelligence Models -- Current State of Research and Practicecited by 14
  12. 2022Diagnosing Ensemble Few-Shot Classifierscited by 13
19 more
  1. 2022A domain-specific language for describing machine learning datasetscited by 12
  2. 2024Dissecting Sample Hardness: A Fine-Grained Analysis of Hardness Characterization Methods for Data-Centric AIcited by 11
  3. 2024Matchmaker: Self-Improving Large Language Model Programs for Schema Matchingcited by 9
  4. 2021Enabling SQL-based Training Data Debugging for Federated Learningcited by 8
  5. 2023TRIAGE: Characterizing and auditing training data for improved regressioncited by 6
  6. 2023VisionKG: Unleashing the Power of Visual Datasets via Knowledge Graphcited by 5
  7. 2023MLOps for Scarce Image Data: A Use Case in Microscopic Image Analysiscited by 4
  8. 2024Towards Trustworthy Machine Learning in Production: An Overview of the Robustness in MLOps Approachcited by 4
  9. 2024You can't handle the (dirty) truth: Data-centric insights improve pseudo-labelingcited by 3
  10. 2023MLHOps: Machine Learning for Healthcare Operationscited by 2
  11. 2021Pastprop-RNN: improved predictions of the future by correcting the pastcited by 1
  12. 2022Designing an Efficient End-to-end Machine Learning Pipeline for Real-time Empty-shelf Detectioncited by 1
  13. 2024DAGnosis: Localized Identification of Data Inconsistencies using Structurescited by 1
  14. 2024Machine Learning Operations: A Mapping Studycited by 1
  15. 2022Active-Learning-as-a-Service: An Automatic and Efficient MLOps System for Data-Centric AIcited by 0
  16. 2022BPMN4sML: A BPMN Extension for Serverless Machine Learning. Technology Independent and Interoperable Modeling of Machine Learning Workflows and their Serverless Deployment Orchestrationcited by 0
  17. 2024Data Quality in Edge Machine Learning: A State-of-the-Art Surveycited by 0
  18. 2025A Data-Centric Perspective on the Influence of Image Data Quality in Machine Learning Modelscited by 0
  19. 2025What's the next frontier for Data-centric AI? Data Savvy Agentscited by 0