# Agile Management for Machine Learning: A Systematic Mapping Study

arXiv: https://arxiv.org/abs/2506.20759 · HTML: https://mlsanity.com/paper/2506.20759/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 40 references that are in the map.
- First posted on arXiv 25 Jun 2025.

## Builds on (most cited first, 7 of 7)

- [AI Lifecycle Models Need To Be Revised. An Exploratory Study in Fintech](https://mlsanity.com/paper/2010.02716/) (2020)
- [A Meta-Summary of Challenges in Building Products with ML Components -- Collecting Experiences from 4758+ Practitioners](https://mlsanity.com/paper/2304.00078/) (2023)
- [Successful Combination of Database Search and Snowballing for Identification of Primary Studies in Systematic Literature Studies](https://mlsanity.com/paper/2307.02612/) (2023)
- [What Makes Agile Software Development Agile?](https://mlsanity.com/paper/2109.11435/) (2021)
- [Analysis of Software Engineering for Agile Machine Learning Projects](https://mlsanity.com/paper/1912.07323/) (2019)
- [STAMP 4 NLP -- An Agile Framework for Rapid Quality-Driven NLP Applications Development](https://mlsanity.com/paper/2111.08408/) (2021)
- [On the Interaction between Software Engineers and Data Scientists when building Machine Learning-Enabled Systems](https://mlsanity.com/paper/2402.05334/) (2024)

## Built on by (most cited first, 1 of 1)

- [AI Assurance in UK Defence: Challenges in Operationalising JSP 936](https://mlsanity.com/paper/2606.09414/) (2026)

## Abstract (arXiv)

[Context] Machine learning (ML)-enabled systems are present in our society, driving significant digital transformations. The dynamic nature of ML development, characterized by experimental cycles and rapid changes in data, poses challenges to traditional project management. Agile methods, with their flexibility and incremental delivery, seem well-suited to address this dynamism. However, it is unclear how to effectively apply these methods in the context of ML-enabled systems, where challenges require tailored approaches. [Goal] Our goal is to outline the state of the art in agile management for ML-enabled systems. [Method] We conducted a systematic mapping study using a hybrid search strategy that combines database searches with backward and forward snowballing iterations. [Results] Our study identified 27 papers published between 2008 and 2024. From these, we identified eight frameworks and categorized recommendations and practices into eight key themes, such as Iteration Flexibility, Innovative ML-specific Artifacts, and the Minimal Viable Model. The main challenge identified across studies was accurate effort estimation for ML-related tasks. [Conclusion] This study contributes by mapping the state of the art and identifying open gaps in the field. While relevant work exists, more robust empirical evaluation is still needed to validate these contributions.