# A Framework to Model ML Engineering Processes

arXiv: https://arxiv.org/abs/2404.18531 · HTML: https://mlsanity.com/paper/2404.18531/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 59 references that are in the map.
- First posted on arXiv 29 Apr 2024.

## Builds on (most cited first, 12 of 12)

- [Machine Learning Operations (MLOps): Overview, Definition, and Architecture](https://mlsanity.com/paper/2205.02302/) (2022)
- [Software Engineering for AI-Based Systems: A Survey](https://mlsanity.com/paper/2105.01984/) (2021)
- [Collaboration Challenges in Building ML-Enabled Systems: Communication, Documentation, Engineering, and Process](https://mlsanity.com/paper/2110.10234/) (2021)
- [Engineering AI Systems: A Research Agenda](https://mlsanity.com/paper/2001.07522/) (2020)
- [Adoption and Effects of Software Engineering Best Practices in Machine Learning](https://mlsanity.com/paper/2007.14130/) (2020)
- [AI Lifecycle Models Need To Be Revised. An Exploratory Study in Fintech](https://mlsanity.com/paper/2010.02716/) (2020)
- [A Software Engineering Perspective on Engineering Machine Learning Systems: State of the Art and Challenges](https://mlsanity.com/paper/2012.07919/) (2020)
- [ML-Schema: Exposing the Semantics of Machine Learning with Schemas and Ontologies](https://mlsanity.com/paper/1807.05351/) (2018)
- [Software engineering for artificial intelligence and machine learning software: A systematic literature review](https://mlsanity.com/paper/2011.03751/) (2020)
- [Developing and Operating Artificial Intelligence Models in Trustworthy Autonomous Systems](https://mlsanity.com/paper/2003.05434/) (2020)
- [From Things' Modeling Language (ThingML) to Things' Machine Learning (ThingML2)](https://mlsanity.com/paper/2009.10632/) (2020)
- [ACCORDANT: A Domain Specific Model and DevOpsApproach for Big Data Analytics Architectures](https://mlsanity.com/paper/2011.08268/) (2020)

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

- [Zero-Shot Embedding Drift Detection: A Lightweight Defense Against Prompt Injections in LLMs](https://mlsanity.com/paper/2601.12359/) (2026)

## Abstract (arXiv)

The development of Machine Learning (ML) based systems is complex and requires multidisciplinary teams with diverse skill sets. This may lead to communication issues or misapplication of best practices. Process models can alleviate these challenges by standardizing task orchestration, providing a common language to facilitate communication, and nurturing a collaborative environment. Unfortunately, current process modeling languages are not suitable for describing the development of such systems. In this paper, we introduce a framework for modeling ML-based software development processes, built around a domain-specific language and derived from an analysis of scientific and gray literature. A supporting toolkit is also available.