# Systematic Mapping Study on the Machine Learning Lifecycle

arXiv: https://arxiv.org/abs/2103.10248 · HTML: https://mlsanity.com/paper/2103.10248/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 19 references that are in the map.
- First posted on arXiv 11 Mar 2021.

## Builds on (most cited first, 6 of 6)

- [Software Engineering Challenges of Deep Learning](https://mlsanity.com/paper/1810.12034/) (2018)
- [AI Lifecycle Models Need To Be Revised. An Exploratory Study in Fintech](https://mlsanity.com/paper/2010.02716/) (2020)
- [Software engineering for artificial intelligence and machine learning software: A systematic literature review](https://mlsanity.com/paper/2011.03751/) (2020)
- [The Key Concepts of Ethics of Artificial Intelligence - A Keyword based Systematic Mapping Study](https://mlsanity.com/paper/1809.07027/) (2018)
- [Benefitting from the Grey Literature in Software Engineering Research](https://mlsanity.com/paper/1911.12038/) (2019)
- [A Systematic Mapping Study on Testing of Machine Learning Programs](https://mlsanity.com/paper/1907.09427/) (2019)

## Built on by (most cited first, 2 of 2)

- [The Pipeline for the Continuous Development of Artificial Intelligence Models -- Current State of Research and Practice](https://mlsanity.com/paper/2301.09001/) (2023)
- [Machine Learning Operations: A Mapping Study](https://mlsanity.com/paper/2409.19416/) (2024)

## Abstract (arXiv)

The development of artificial intelligence (AI) has made various industries eager to explore the benefits of AI. There is an increasing amount of research surrounding AI, most of which is centred on the development of new AI algorithms and techniques. However, the advent of AI is bringing an increasing set of practical problems related to AI model lifecycle management that need to be investigated. We address this gap by conducting a systematic mapping study on the lifecycle of AI model. Through quantitative research, we provide an overview of the field, identify research opportunities, and provide suggestions for future research. Our study yields 405 publications published from 2005 to 2020, mapped in 5 different main research topics, and 31 sub-topics. We observe that only a minority of publications focus on data management and model production problems, and that more studies should address the AI lifecycle from a holistic perspective.