# Capturing Dependencies within Machine Learning via a Formal Process Model

arXiv: https://arxiv.org/abs/2208.05219 · HTML: https://mlsanity.com/paper/2208.05219/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 43 references that are in the map.
- First posted on arXiv 10 Aug 2022.

## Builds on (most cited first, 13 of 13)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [AutoML: A Survey of the State-of-the-Art](https://mlsanity.com/paper/1908.00709/) (2019)
- [Data and its (dis)contents: A survey of dataset development and use in machine learning research](https://mlsanity.com/paper/2012.05345/) (2020)
- [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)
- [Measuring the Algorithmic Efficiency of Neural Networks](https://mlsanity.com/paper/2005.04305/) (2020)
- [Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology](https://mlsanity.com/paper/2003.05155/) (2020)
- [Engineering AI Systems: A Research Agenda](https://mlsanity.com/paper/2001.07522/) (2020)
- [A Software Engineering Perspective on Engineering Machine Learning Systems: State of the Art and Challenges](https://mlsanity.com/paper/2012.07919/) (2020)
- [Machine Learning Software Engineering in Practice: An Industrial Case Study](https://mlsanity.com/paper/1906.07154/) (2019)
- [Characterizing machine learning process: A maturity framework](https://mlsanity.com/paper/1811.04871/) (2018)
- [Preliminary Systematic Literature Review of Machine Learning System Development Process](https://mlsanity.com/paper/1910.05528/) (2019)
- [Acoustic Leak Detection in Water Networks](https://mlsanity.com/paper/2012.06280/) (2020)

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

- [Towards a safe MLOps Process for the Continuous Development and Safety Assurance of ML-based Systems in the Railway Domain](https://mlsanity.com/paper/2307.02867/) (2023)

## Abstract (arXiv)

The development of Machine Learning (ML) models is more than just a special case of software development (SD): ML models acquire properties and fulfill requirements even without direct human interaction in a seemingly uncontrollable manner. Nonetheless, the underlying processes can be described in a formal way. We define a comprehensive SD process model for ML that encompasses most tasks and artifacts described in the literature in a consistent way. In addition to the production of the necessary artifacts, we also focus on generating and validating fitting descriptions in the form of specifications. We stress the importance of further evolving the ML model throughout its life-cycle even after initial training and testing. Thus, we provide various interaction points with standard SD processes in which ML often is an encapsulated task. Further, our SD process model allows to formulate ML as a (meta-) optimization problem. If automated rigorously, it can be used to realize self-adaptive autonomous systems. Finally, our SD process model features a description of time that allows to reason about the progress within ML development processes. This might lead to further applications of formal methods within the field of ML.