# Building Domain-Specific Machine Learning Workflows: A Conceptual Framework for the State-of-the-Practice

arXiv: https://arxiv.org/abs/2203.08638 · HTML: https://mlsanity.com/paper/2203.08638/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2022.

## Built on by (most cited first, 8 of 8)

- [LAMBDA: A Large Model Based Data Agent](https://mlsanity.com/paper/2407.17535/) (2024)
- [Large Language Models for Constructing and Optimizing Machine Learning Workflows: A Survey](https://mlsanity.com/paper/2411.10478/) (2024)
- [Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance](https://mlsanity.com/paper/2506.23314/) (2025)
- [Modeling User-System Behavior for Training-free Building of Private Domain Conversational Agents](https://mlsanity.com/paper/2511.10283/) (2025)
- [Assesing the Viability of Unsupervised Learning with Autoencoders for Predictive Maintenance in Helicopter Engines](https://mlsanity.com/paper/2601.11154/) (2026)
- [Designing FSMs Specifications from Requirements with GPT 4.0](https://mlsanity.com/paper/2603.29140/) (2026)
- [From Intent to AI Pipelines: A Controlled Agentic Framework for Non-AI Expert Scientists](https://mlsanity.com/paper/2605.18764/) (2026)
- [Building real-time digital twin instances with Function+Data Flow: user evaluation and extension for iterative pipelines](https://mlsanity.com/paper/2608.18480/) (2026)

## Abstract (arXiv)

Domain experts are increasingly employing machine learning to solve their domain-specific problems. This article presents six key challenges that a domain expert faces in transforming their problem into a computational workflow, and then into an executable implementation. These challenges arise out of our conceptual framework which presents the "route" of options that a domain expert may choose to take while developing their solution. To ground our conceptual framework in the state-of-the-practice, this article discusses a selection of available textual and graphical workflow systems and their support for these six challenges. Case studies from the literature in various domains are also examined to highlight the tools used by the domain experts as well as a classification of the domain-specificity and machine learning usage of their problem, workflow, and implementation. The state-of-the-practice informs our discussion of the six key challenges, where we identify which challenges are not sufficiently addressed by available tools. We also suggest possible research directions for software engineering researchers to increase the automation of these tools and disseminate best-practice techniques between software engineering and various scientific domains.