mlsanityData to 13 Oct 2026

MLOps: A Review

arXiv 2308.10908 ↗First posted 19 Aug 2023cs.LG cs.SEcited by 16

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Abstract (arXiv)

Recently, Machine Learning (ML) has become a widely accepted method for significant progress that is rapidly evolving. Since it employs computational methods to teach machines and produce acceptable answers. The significance of the Machine Learning Operations (MLOps) methods, which can provide acceptable answers for such problems, is examined in this study. To assist in the creation of software that is simple to use, the authors research MLOps methods. To choose the best tool structure for certain projects, the authors also assess the features and operability of various MLOps methods. A total of 22 papers were assessed that attempted to apply the MLOps idea. Finally, the authors admit the scarcity of fully effective MLOps methods based on which advancements can self-regulate by limiting human engagement.

Key facts. Cited by 16 papers in the map (data through 13 Oct 2026). Builds on 14 of its 53 references that are in the map. First posted on arXiv 19 Aug 2023.

Builds on

14 of 53 references.

References · 14

  1. 2020Challenges in Deploying Machine Learning: a Survey of Case Studiescited by 152
  2. 2022Machine Learning Operations (MLOps): Overview, Definition, and Architecturecited by 127
  3. 2021Who Needs MLOps: What Data Scientists Seek to Accomplish and How Can MLOps Help?cited by 37
  4. 2021A Data Quality-Driven View of MLOpscited by 31
  5. 2022MLOps -- Definitions, Tools and Challengescited by 25
  6. 2019A Survey of DevOps Concepts and Challengescited by 12
  7. 2021MLOps Challenges in Multi-Organization Setup: Experiences from Two Real-World Casescited by 10
  8. 2021SensiX++: Bringing MLOPs and Multi-tenant Model Serving to Sensory Edge Devicescited by 7
  9. 2022Machine Learning Operations: A Survey on MLOps Tool Supportcited by 7
  10. 2020The Collective Knowledge project: making ML models more portable and reproducible with open APIs, reusable best practices and MLOpscited by 4
  11. 2021Agility in Software 2.0 -- Notebook Interfaces and MLOps with Buttresses and Rebarscited by 4
  12. 2022TinyMLOps: Operational Challenges for Widespread Edge AI Adoptioncited by 3
2 more
  1. 2020CodeReef: an open platform for portable MLOps, reusable automation actions and reproducible benchmarkingcited by 2
  2. 2021Scaling Enterprise Recommender Systems for Decentralizationcited by 1

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Citing papers per year: 2023: 1; 2024: 12; 2025: 3.

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4 more
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