mlsanityData to 7 Oct 2026

Machine Learning Operations (MLOps): Overview, Definition, and Architecture

arXiv 2205.02302 ↗First posted 4 May 2022cs.LGcited by 126

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Abstract (arXiv)

The final goal of all industrial machine learning (ML) projects is to develop ML products and rapidly bring them into production. However, it is highly challenging to automate and operationalize ML products and thus many ML endeavors fail to deliver on their expectations. The paradigm of Machine Learning Operations (MLOps) addresses this issue. MLOps includes several aspects, such as best practices, sets of concepts, and development culture. However, MLOps is still a vague term and its consequences for researchers and professionals are ambiguous. To address this gap, we conduct mixed-method research, including a literature review, a tool review, and expert interviews. As a result of these investigations, we provide an aggregated overview of the necessary principles, components, and roles, as well as the associated architecture and workflows. Furthermore, we furnish a definition of MLOps and highlight open challenges in the field. Finally, this work provides guidance for ML researchers and practitioners who want to automate and operate their ML products with a designated set of technologies.

Key facts. Cited by 126 papers in the map (data through 7 Oct 2026). Builds on 9 of its 67 references that are in the map. First posted on arXiv 4 May 2022.

Builds on

9 of 67 references.

References · 9

  1. 2015Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Codingcited by 2,291
  2. 2016Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activationscited by 413
  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. 2021Detecting Concept Drift With Neural Network Model Uncertaintycited by 21
  6. 2017SCARFF: a Scalable Framework for Streaming Credit Card Fraud Detection with Sparkcited by 14
  7. 2019A Survey of DevOps Concepts and Challengescited by 12
  8. 2021MLOps Challenges in Multi-Organization Setup: Experiences from Two Real-World Casescited by 10
  9. 2022Machine Learning Operations: A Survey on MLOps Tool Supportcited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 6; 2023: 27; 2024: 28; 2025: 30; 2026: 35.

126 papers.

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  1. 2023Continual Learning: Applications and the Road Forwardcited by 59
  2. 2022Data-Centric Artificial Intelligencecited by 31
  3. 2022A Validity Perspective on Evaluating the Justified Use of Data-driven Decision-making Algorithmscited by 30
  4. 2022Operationalizing Machine Learning: An Interview Studycited by 25
  5. 2023MLOps: A Reviewcited by 16
  6. 2023Towards Data-centric Graph Machine Learning: Review and Outlookcited by 15
  7. 2023The Pipeline for the Continuous Development of Artificial Intelligence Models -- Current State of Research and Practicecited by 14
  8. 2023Efficient Neural Networks for Tiny Machine Learning: A Comprehensive Reviewcited by 9
  9. 2023Moderating Model Marketplaces: Platform Governance Puzzles for AI Intermediariescited by 9
  10. 2023Machine Learning with Requirements: a Manifestocited by 8
  11. 2024Continuous fake media detection: adapting deepfake detectors to new generative techniquescited by 6
  12. 2024On the Workflows and Smells of Leaderboard Operations (LBOps): An Exploratory Study of Foundation Model Leaderboardscited by 6
38 more
  1. 2025Agentic Services Computingcited by 6
  2. 2022Continual-Learning-as-a-Service (CLaaS): On-Demand Efficient Adaptation of Predictive Modelscited by 5
  3. 2023Towards Message Brokers for Generative AI: Survey, Challenges, and Opportunitiescited by 5
  4. 2023Privacy-preserving machine learning for healthcare: open challenges and future perspectivescited by 4
  5. 2023End-User Development for Artificial Intelligence: A Systematic Literature Reviewcited by 4
  6. 2023MLOps for Scarce Image Data: A Use Case in Microscopic Image Analysiscited by 4
  7. 2023OceanBench: The Sea Surface Height Editioncited by 4
  8. 2024Is Your AI Truly Yours? Leveraging Blockchain for Copyrights, Provenance, and Lineagecited by 4
  9. 2024Adaptive Data Quality Scoring Operations Framework using Drift-Aware Mechanism for Industrial Applicationscited by 4
  10. 2024Towards certification: A complete statistical validation pipeline for supervised learning in industrycited by 4
  11. 2023ML-Based Teaching Systems: A Conceptual Frameworkcited by 3
  12. 2023sQUlearn -- A Python Library for Quantum Machine Learningcited by 3
  13. 2024XAIport: A Service Framework for the Early Adoption of XAI in AI Model Developmentcited by 3
  14. 2024Survey for Landing Generative AI in Social and E-commerce Recsys -- the Industry Perspectivescited by 3
  15. 2024Towards Trustworthy Machine Learning in Production: An Overview of the Robustness in MLOps Approachcited by 3
  16. 2024Assessing the Auditability of AI-integrating Systems: A Framework and Learning Analytics Case Studycited by 3
  17. 2025Securing RAG: A Risk Assessment and Mitigation Frameworkcited by 3
  18. 2025Towards Secure MLOps: Surveying Attacks, Mitigation Strategies, and Research Challengescited by 3
  19. 2026From Prompts to Contracts: Harness Engineering for Auditable Enterprise LLM Agentscited by 3
  20. 2023Scalable End-to-End ML Platforms: from AutoML to Self-servecited by 2
  21. 2023MLHOps: Machine Learning for Healthcare Operationscited by 2
  22. 2023Towards Regulatable AI Systems: Technical Gaps and Policy Opportunitiescited by 2
  23. 2024Wasserstein Distributionally Robust Shallow Convex Neural Networkscited by 2
  24. 2024Adapting MLOps for Diverse In-Network Intelligence in 6G Era: Challenges and Solutionscited by 2
  25. 2025LoCoML: A Framework for Real-World ML Inference Pipelinescited by 2
  26. 2025Towards deployment-centric multimodal AI beyond vision and languagecited by 2
  27. 2025MLOps with Microservices: A Case Study on the Maritime Domaincited by 2
  28. 2025TechOps: Technical Documentation Templates for the AI Actcited by 2
  29. 2026The Semi-Executable Stack: Agentic Software Engineering and the Expanding Scope of SEcited by 2
  30. 2026A Category Theory Account of AI Identitycited by 2
  31. 2026The Moving Target: A Longitudinal Audit of Trust-Benchmark Score Drift Across Open-Source Chat LLM Release Linescited by 2
  32. 2022Capturing Dependencies within Machine Learning via a Formal Process Modelcited by 1
  33. 2023Mithridates: Auditing and Boosting Backdoor Resistance of Machine Learning Pipelinescited by 1
  34. 2023Self-Improving-Leaderboard(SIL): A Call for Real-World Centric Natural Language Processing Leaderboardscited by 1
  35. 2023A Comprehensive Study of Groundbreaking Machine Learning Research: Analyzing highly cited and impactful publications across six decadescited by 1
  36. 2023On-Premise AIOps Infrastructure for a Software Editor SME: An Experience Reportcited by 1
  37. 2023Speculative Exploration on the Concept of Artificial Agents Conducting Autonomous Researchcited by 1
  38. 2024A Framework to Model ML Engineering Processescited by 1

50 most cited of 126 All 126 (JSON)