# Machine Learning Operations: A Survey on MLOps Tool Support

arXiv: https://arxiv.org/abs/2202.10169 · HTML: https://mlsanity.com/paper/2202.10169/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2022.

## Built on by (most cited first, 7 of 7)

- [Machine Learning Operations (MLOps): Overview, Definition, and Architecture](https://mlsanity.com/paper/2205.02302/) (2022)
- [MLOps: A Review](https://mlsanity.com/paper/2308.10908/) (2023)
- [Continual Learning for Predictive Maintenance: Overview and Challenges](https://mlsanity.com/paper/2301.12467/) (2023)
- [Continual-Learning-as-a-Service (CLaaS): On-Demand Efficient Adaptation of Predictive Models](https://mlsanity.com/paper/2206.06957/) (2022)
- [Towards Trustworthy Machine Learning in Production: An Overview of the Robustness in MLOps Approach](https://mlsanity.com/paper/2410.21346/) (2024)
- [Towards MLOps: A DevOps Tools Recommender System for Machine Learning System](https://mlsanity.com/paper/2402.12867/) (2024)
- [KModels: Unlocking AI for Business Applications](https://mlsanity.com/paper/2409.05919/) (2024)

## Abstract (arXiv)

Machine Learning (ML) has become a fast-growing, trending approach in solution development in practice. Deep Learning (DL) which is a subset of ML, learns using deep neural networks to simulate the human brain. It trains machines to learn techniques and processes individually using computer algorithms, which is also considered to be a role of Artificial Intelligence (AI). In this paper, we study current technical issues related to software development and delivery in organizations that work on ML projects. Therefore, the importance of the Machine Learning Operations (MLOps) concept, which can deliver appropriate solutions for such concerns, is discussed. We investigate commercially available MLOps tool support in software development. The comparison between MLOps tools analyzes the performance of each system and its use cases. Moreover, we examine the features and usability of MLOps tools to identify the most appropriate tool support for given scenarios. Finally, we recognize that there is a shortage in the availability of a fully functional MLOps platform on which processes can be automated by reducing human intervention.