# Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology

arXiv: https://arxiv.org/abs/2003.05155 · HTML: https://mlsanity.com/paper/2003.05155/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Mar 2020.

## Built on by (most cited first, 30 of 41)

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- [Machine Learning with Requirements: a Manifesto](https://mlsanity.com/paper/2304.03674/) (2023)
- [Quality issues in Machine Learning Software Systems](https://mlsanity.com/paper/2208.08982/) (2022)
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- [Certification of embedded systems based on Machine Learning: A survey](https://mlsanity.com/paper/2106.07221/) (2021)
- [Artificial Intelligence in Industry 4.0: A Review of Integration Challenges for Industrial Systems](https://mlsanity.com/paper/2405.18580/) (2024)
- [The Thousand Faces of Explainable AI Along the Machine Learning Life Cycle: Industrial Reality and Current State of Research](https://mlsanity.com/paper/2310.07882/) (2023)
- [Explanatory Model Monitoring to Understand the Effects of Feature Shifts on Performance](https://mlsanity.com/paper/2408.13648/) (2024)
- [Adversarial Threat Vectors and Risk Mitigation for Retrieval-Augmented Generation Systems](https://mlsanity.com/paper/2506.00281/) (2025)
- [AI Hazard Management: A framework for the systematic management of root causes for AI risks](https://mlsanity.com/paper/2310.16727/) (2023)
- [XAIport: A Service Framework for the Early Adoption of XAI in AI Model Development](https://mlsanity.com/paper/2403.16858/) (2024)
- [Machine Learning in Finance-Emerging Trends and Challenges](https://mlsanity.com/paper/2110.11999/) (2021)
- [Modeling Quality and Machine Learning Pipelines through Extended Feature Models](https://mlsanity.com/paper/2207.07528/) (2022)
- [MLTEing Models: Negotiating, Evaluating, and Documenting Model and System Qualities](https://mlsanity.com/paper/2303.01998/) (2023)
- [Benchmarking Automated Machine Learning Methods for Price Forecasting Applications](https://mlsanity.com/paper/2304.14735/) (2023)
- [Dynamic Priors in Bayesian Optimization for Hyperparameter Optimization](https://mlsanity.com/paper/2511.02570/) (2025)
- [STAMP 4 NLP -- An Agile Framework for Rapid Quality-Driven NLP Applications Development](https://mlsanity.com/paper/2111.08408/) (2021)
- [Capturing Dependencies within Machine Learning via a Formal Process Model](https://mlsanity.com/paper/2208.05219/) (2022)
- [A Graphical Modeling Language for Artificial Intelligence Applications in Automation Systems](https://mlsanity.com/paper/2306.11767/) (2023)
- [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)
- [Offensive Security for AI Systems: Concepts, Practices, and Applications](https://mlsanity.com/paper/2505.06380/) (2025)
- [Before the Clinic: Transparent and Operable Design Principles for Healthcare AI](https://mlsanity.com/paper/2511.01902/) (2025)
- [Quality Issues in Machine Learning Software Systems](https://mlsanity.com/paper/2306.15007/) (2023)
- [Choosing the Right Path for AI Integration in Engineering Companies: A Strategic Guide](https://mlsanity.com/paper/2402.00011/) (2023)
- [Research information in the light of artificial intelligence: quality and data ecologies](https://mlsanity.com/paper/2405.12997/) (2024)
- [Design Principles for Falsifiable, Replicable and Reproducible Empirical ML Research](https://mlsanity.com/paper/2405.18077/) (2024)
- [Data Quality Awareness: A Journey from Traditional Data Management to Data Science Systems](https://mlsanity.com/paper/2411.03007/) (2024)

## Abstract (arXiv)

Machine learning is an established and frequently used technique in industry and academia but a standard process model to improve success and efficiency of machine learning applications is still missing. Project organizations and machine learning practitioners have a need for guidance throughout the life cycle of a machine learning application to meet business expectations. We therefore propose a process model for the development of machine learning applications, that covers six phases from defining the scope to maintaining the deployed machine learning application. The first phase combines business and data understanding as data availability oftentimes affects the feasibility of the project. The sixth phase covers state-of-the-art approaches for monitoring and maintenance of a machine learning applications, as the risk of model degradation in a changing environment is eminent. With each task of the process, we propose quality assurance methodology that is suitable to adress challenges in machine learning development that we identify in form of risks. The methodology is drawn from practical experience and scientific literature and has proven to be general and stable. The process model expands on CRISP-DM, a data mining process model that enjoys strong industry support but lacks to address machine learning specific tasks. Our work proposes an industry and application neutral process model tailored for machine learning applications with focus on technical tasks for quality assurance.