# MLOps: A Review

arXiv: https://arxiv.org/abs/2308.10908 · HTML: https://mlsanity.com/paper/2308.10908/

## Key facts

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

## Builds on (most cited first, 14 of 14)

- [Challenges in Deploying Machine Learning: a Survey of Case Studies](https://mlsanity.com/paper/2011.09926/) (2020)
- [Machine Learning Operations (MLOps): Overview, Definition, and Architecture](https://mlsanity.com/paper/2205.02302/) (2022)
- [Who Needs MLOps: What Data Scientists Seek to Accomplish and How Can MLOps Help?](https://mlsanity.com/paper/2103.08942/) (2021)
- [A Data Quality-Driven View of MLOps](https://mlsanity.com/paper/2102.07750/) (2021)
- [MLOps -- Definitions, Tools and Challenges](https://mlsanity.com/paper/2201.00162/) (2022)
- [A Survey of DevOps Concepts and Challenges](https://mlsanity.com/paper/1909.05409/) (2019)
- [MLOps Challenges in Multi-Organization Setup: Experiences from Two Real-World Cases](https://mlsanity.com/paper/2103.08937/) (2021)
- [SensiX++: Bringing MLOPs and Multi-tenant Model Serving to Sensory Edge Devices](https://mlsanity.com/paper/2109.03947/) (2021)
- [Machine Learning Operations: A Survey on MLOps Tool Support](https://mlsanity.com/paper/2202.10169/) (2022)
- [The Collective Knowledge project: making ML models more portable and reproducible with open APIs, reusable best practices and MLOps](https://mlsanity.com/paper/2006.07161/) (2020)
- [Agility in Software 2.0 -- Notebook Interfaces and MLOps with Buttresses and Rebars](https://mlsanity.com/paper/2111.14142/) (2021)
- [TinyMLOps: Operational Challenges for Widespread Edge AI Adoption](https://mlsanity.com/paper/2203.10923/) (2022)
- [CodeReef: an open platform for portable MLOps, reusable automation actions and reproducible benchmarking](https://mlsanity.com/paper/2001.07935/) (2020)
- [Scaling Enterprise Recommender Systems for Decentralization](https://mlsanity.com/paper/2109.09231/) (2021)

## Built on by (most cited first, 16 of 16)

- [Enhancing End-to-End Multi-Task Dialogue Systems: A Study on Intrinsic Motivation Reinforcement Learning Algorithms for Improved Training and Adaptability](https://mlsanity.com/paper/2401.18040/) (2024)
- [Advancing Household Robotics: Deep Interactive Reinforcement Learning for Efficient Training and Enhanced Performance](https://mlsanity.com/paper/2405.18687/) (2024)
- [Code Reborn AI-Driven Legacy Systems Modernization from COBOL to Java](https://mlsanity.com/paper/2504.11335/) (2025)
- [The Emotional Impact of Game Duration: A Framework for Understanding Player Emotions in Extended Gameplay Sessions](https://mlsanity.com/paper/2404.00526/) (2024)
- [A Novel Audio Representation for Music Genre Identification in MIR](https://mlsanity.com/paper/2404.01058/) (2024)
- [Unveiling the Impact of Macroeconomic Policies: A Double Machine Learning Approach to Analyzing Interest Rate Effects on Financial Markets](https://mlsanity.com/paper/2404.07225/) (2024)
- [Efficient Market Dynamics: Unraveling Informational Efficiency in UK Horse Racing Betting Markets Through Betfair's Time Series Analysis](https://mlsanity.com/paper/2402.02623/) (2024)
- [The Evolution of Football Betting- A Machine Learning Approach to Match Outcome Forecasting and Bookmaker Odds Estimation](https://mlsanity.com/paper/2403.16282/) (2024)
- [Enhancing Music Genre Classification through Multi-Algorithm Analysis and User-Friendly Visualization](https://mlsanity.com/paper/2405.17413/) (2024)
- [From Text to Trends: A Unique Garden Analytics Perspective on the Future of Modern Agriculture](https://mlsanity.com/paper/2309.12579/) (2023)
- [Analyzing Currency Fluctuations: A Comparative Study of GARCH, EWMA, and IV Models for GBP/USD and EUR/GBP Pairs](https://mlsanity.com/paper/2402.07435/) (2024)
- [Enhancing Mean-Reverting Time Series Prediction with Gaussian Processes: Functional and Augmented Data Structures in Financial Forecasting](https://mlsanity.com/paper/2403.00796/) (2024)
- [Exploring Quantum-Enhanced Machine Learning for Computer Vision: Applications and Insights on Noisy Intermediate-Scale Quantum Devices](https://mlsanity.com/paper/2404.02177/) (2024)
- [Optimizing Photometric Light Curve Analysis: Evaluating Scipy's Minimize Function for Eclipse Mapping of Cataclysmic Variables](https://mlsanity.com/paper/2406.00071/) (2024)
- [Proposing a Framework for Machine Learning Adoption on Legacy Systems](https://mlsanity.com/paper/2509.24224/) (2025)
- [Model Gateway: Management Platform for Model-Driven Drug Discovery](https://mlsanity.com/paper/2512.05462/) (2025)

## 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.