# A Data Quality-Driven View of MLOps

arXiv: https://arxiv.org/abs/2102.07750 · HTML: https://mlsanity.com/paper/2102.07750/

## 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 (most cited first, 13 of 13)

- [Snorkel: Rapid Training Data Creation with Weak Supervision](https://mlsanity.com/paper/1711.10160/) (2017)
- [The Ladder: A Reliable Leaderboard for Machine Learning Competitions](https://mlsanity.com/paper/1502.04585/) (2015)
- [Generative Adversarial Networks recover features in astrophysical images of galaxies beyond the deconvolution limit](https://mlsanity.com/paper/1702.00403/) (2017)
- [Overton: A Data System for Monitoring and Improving Machine-Learned Products](https://mlsanity.com/paper/1909.05372/) (2019)
- [Nearest Neighbor Classifiers over Incomplete Information: From Certain Answers to Certain Predictions](https://mlsanity.com/paper/2005.05117/) (2020)
- [Online Active Model Selection for Pre-trained Classifiers](https://mlsanity.com/paper/2010.09818/) (2020)
- [Using transfer learning to detect galaxy mergers](https://mlsanity.com/paper/1805.10289/) (2018)
- [Continuous Integration of Machine Learning Models with ease.ml/ci: Towards a Rigorous Yet Practical Treatment](https://mlsanity.com/paper/1903.00278/) (2019)
- [Patient Risk Assessment and Warning Symptom Detection Using Deep Attention-Based Neural Networks](https://mlsanity.com/paper/1809.10804/) (2018)
- [CleanML: A Study for Evaluating the Impact of Data Cleaning on ML Classification Tasks](https://mlsanity.com/paper/1904.09483/) (2019)
- [On Convergence of Nearest Neighbor Classifiers over Feature Transformations](https://mlsanity.com/paper/2010.07765/) (2020)
- [Exploring galaxy evolution with generative models](https://mlsanity.com/paper/1812.01114/) (2018)
- [Sensing Social Media Signals for Cryptocurrency News](https://mlsanity.com/paper/1903.11451/) (2019)

## Built on by (most cited first, 30 of 31)

- [Machine Learning Operations (MLOps): Overview, Definition, and Architecture](https://mlsanity.com/paper/2205.02302/) (2022)
- [Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective](https://mlsanity.com/paper/2112.06409/) (2021)
- [Data-Centric Artificial Intelligence](https://mlsanity.com/paper/2212.11854/) (2022)
- [MLOps -- Definitions, Tools and Challenges](https://mlsanity.com/paper/2201.00162/) (2022)
- [Operationalizing Machine Learning: An Interview Study](https://mlsanity.com/paper/2209.09125/) (2022)
- [Reimagining Synthetic Tabular Data Generation through Data-Centric AI: A Comprehensive Benchmark](https://mlsanity.com/paper/2310.16981/) (2023)
- [Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular data](https://mlsanity.com/paper/2210.13043/) (2022)
- [MLOps: A Review](https://mlsanity.com/paper/2308.10908/) (2023)
- [RLOps: Development Life-cycle of Reinforcement Learning Aided Open RAN](https://mlsanity.com/paper/2111.06978/) (2021)
- [DC-Check: A Data-Centric AI checklist to guide the development of reliable machine learning systems](https://mlsanity.com/paper/2211.05764/) (2022)
- [The Pipeline for the Continuous Development of Artificial Intelligence Models -- Current State of Research and Practice](https://mlsanity.com/paper/2301.09001/) (2023)
- [Diagnosing Ensemble Few-Shot Classifiers](https://mlsanity.com/paper/2206.04372/) (2022)
- [A domain-specific language for describing machine learning datasets](https://mlsanity.com/paper/2207.02848/) (2022)
- [Dissecting Sample Hardness: A Fine-Grained Analysis of Hardness Characterization Methods for Data-Centric AI](https://mlsanity.com/paper/2403.04551/) (2024)
- [Matchmaker: Self-Improving Large Language Model Programs for Schema Matching](https://mlsanity.com/paper/2410.24105/) (2024)
- [Enabling SQL-based Training Data Debugging for Federated Learning](https://mlsanity.com/paper/2108.11884/) (2021)
- [TRIAGE: Characterizing and auditing training data for improved regression](https://mlsanity.com/paper/2310.18970/) (2023)
- [VisionKG: Unleashing the Power of Visual Datasets via Knowledge Graph](https://mlsanity.com/paper/2309.13610/) (2023)
- [MLOps for Scarce Image Data: A Use Case in Microscopic Image Analysis](https://mlsanity.com/paper/2309.15521/) (2023)
- [Towards Trustworthy Machine Learning in Production: An Overview of the Robustness in MLOps Approach](https://mlsanity.com/paper/2410.21346/) (2024)
- [You can't handle the (dirty) truth: Data-centric insights improve pseudo-labeling](https://mlsanity.com/paper/2406.13733/) (2024)
- [MLHOps: Machine Learning for Healthcare Operations](https://mlsanity.com/paper/2305.02474/) (2023)
- [Pastprop-RNN: improved predictions of the future by correcting the past](https://mlsanity.com/paper/2106.13881/) (2021)
- [Designing an Efficient End-to-end Machine Learning Pipeline for Real-time Empty-shelf Detection](https://mlsanity.com/paper/2205.13060/) (2022)
- [DAGnosis: Localized Identification of Data Inconsistencies using Structures](https://mlsanity.com/paper/2402.17599/) (2024)
- [Machine Learning Operations: A Mapping Study](https://mlsanity.com/paper/2409.19416/) (2024)
- [Active-Learning-as-a-Service: An Automatic and Efficient MLOps System for Data-Centric AI](https://mlsanity.com/paper/2207.09109/) (2022)
- [BPMN4sML: A BPMN Extension for Serverless Machine Learning. Technology Independent and Interoperable Modeling of Machine Learning Workflows and their Serverless Deployment Orchestration](https://mlsanity.com/paper/2208.02030/) (2022)
- [Data Quality in Edge Machine Learning: A State-of-the-Art Survey](https://mlsanity.com/paper/2406.02600/) (2024)
- [A Data-Centric Perspective on the Influence of Image Data Quality in Machine Learning Models](https://mlsanity.com/paper/2509.24420/) (2025)

## 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.