# Who Needs MLOps: What Data Scientists Seek to Accomplish and How Can MLOps Help?

arXiv: https://arxiv.org/abs/2103.08942 · HTML: https://mlsanity.com/paper/2103.08942/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2021.

## Built on by (most cited first, 30 of 37)

- [Trustworthy AI: From Principles to Practices](https://mlsanity.com/paper/2110.01167/) (2021)
- [Machine Learning Operations (MLOps): Overview, Definition, and Architecture](https://mlsanity.com/paper/2205.02302/) (2022)
- [Collaboration Challenges in Building ML-Enabled Systems: Communication, Documentation, Engineering, and Process](https://mlsanity.com/paper/2110.10234/) (2021)
- [Sensible AI: Re-imagining Interpretability and Explainability using Sensemaking Theory](https://mlsanity.com/paper/2205.05057/) (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)
- [A Meta-Summary of Challenges in Building Products with ML Components -- Collecting Experiences from 4758+ Practitioners](https://mlsanity.com/paper/2304.00078/) (2023)
- [MLOps: A Review](https://mlsanity.com/paper/2308.10908/) (2023)
- [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)
- [Overcoming the Stability Gap in Continual Learning](https://mlsanity.com/paper/2306.01904/) (2023)
- [A Human-Centric Take on Model Monitoring](https://mlsanity.com/paper/2206.02868/) (2022)
- [Machine Learning with Requirements: a Manifesto](https://mlsanity.com/paper/2304.03674/) (2023)
- [Putting AI Ethics into Practice: The Hourglass Model of Organizational AI Governance](https://mlsanity.com/paper/2206.00335/) (2022)
- [Towards Trustworthy Machine Learning in Production: An Overview of the Robustness in MLOps Approach](https://mlsanity.com/paper/2410.21346/) (2024)
- [Capabilities for Better ML Engineering](https://mlsanity.com/paper/2211.06409/) (2022)
- [Towards Secure MLOps: Surveying Attacks, Mitigation Strategies, and Research Challenges](https://mlsanity.com/paper/2506.02032/) (2025)
- [Disease Progression Modeling Workbench 360](https://mlsanity.com/paper/2106.13265/) (2021)
- [A Preliminary Investigation of MLOps Practices in GitHub](https://mlsanity.com/paper/2209.11453/) (2022)
- [Scalable End-to-End ML Platforms: from AutoML to Self-serve](https://mlsanity.com/paper/2302.14139/) (2023)
- [MLHOps: Machine Learning for Healthcare Operations](https://mlsanity.com/paper/2305.02474/) (2023)
- [MLOps: A Step Forward to Enterprise Machine Learning](https://mlsanity.com/paper/2305.19298/) (2023)
- [Measuring Distributional Shifts in Text: The Advantage of Language Model-Based Embeddings](https://mlsanity.com/paper/2312.02337/) (2023)
- [Towards MLOps: A DevOps Tools Recommender System for Machine Learning System](https://mlsanity.com/paper/2402.12867/) (2024)
- [Designing an Efficient End-to-end Machine Learning Pipeline for Real-time Empty-shelf Detection](https://mlsanity.com/paper/2205.13060/) (2022)
- [AI for Agile development: a Meta-Analysis](https://mlsanity.com/paper/2305.08093/) (2023)
- [Exploratory Visual Analysis for Increasing Data Readiness in Artificial Intelligence Projects](https://mlsanity.com/paper/2409.03805/) (2024)
- [OML-AD: Online Machine Learning for Anomaly Detection in Time Series Data](https://mlsanity.com/paper/2409.09742/) (2024)
- [Machine Learning Operations: A Mapping Study](https://mlsanity.com/paper/2409.19416/) (2024)
- [Opportunities for Machine Learning to Accelerate Halide Perovskite Commercialization and Scale-Up](https://mlsanity.com/paper/2110.03923/) (2021)

## Abstract (arXiv)

Following continuous software engineering practices, there has been an increasing interest in rapid deployment of machine learning (ML) features, called MLOps. In this paper, we study the importance of MLOps in the context of data scientists' daily activities, based on a survey where we collected responses from 331 professionals from 63 different countries in ML domain, indicating on what they were working on in the last three months. Based on the results, up to 40% respondents say that they work with both models and infrastructure; the majority of the work revolves around relational and time series data; and the largest categories of problems to be solved are predictive analysis, time series data, and computer vision. The biggest perceived problems revolve around data, although there is some awareness of problems related to deploying models to production and related procedures. To hypothesise, we believe that organisations represented in the survey can be divided to three categories -- (i) figuring out how to best use data; (ii) focusing on building the first models and getting them to production; and (iii) managing several models, their versions and training datasets, as well as retraining and frequent deployment of retrained models. In the results, the majority of respondents are in category (i) or (ii), focusing on data and models; however the benefits of MLOps only emerge in category (iii) when there is a need for frequent retraining and redeployment. Hence, setting up an MLOps pipeline is a natural step to take, when an organization takes the step from ML as a proof-of-concept to ML as a part of nominal activities.