# A Meta-Summary of Challenges in Building Products with ML Components -- Collecting Experiences from 4758+ Practitioners

arXiv: https://arxiv.org/abs/2304.00078 · HTML: https://mlsanity.com/paper/2304.00078/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 45 of its 140 references that are in the map.
- First posted on arXiv 31 Mar 2023.

## Builds on (most cited first, 30 of 45)

- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Beyond Accuracy: Behavioral Testing of NLP models with CheckList](https://mlsanity.com/paper/2005.04118/) (2020)
- [Improving fairness in machine learning systems: What do industry practitioners need?](https://mlsanity.com/paper/1812.05239/) (2018)
- [Machine Learning Testing: Survey, Landscapes and Horizons](https://mlsanity.com/paper/1906.10742/) (2019)
- [Explainable Machine Learning in Deployment](https://mlsanity.com/paper/1909.06342/) (2019)
- [Challenges in Deploying Machine Learning: a Survey of Case Studies](https://mlsanity.com/paper/2011.09926/) (2020)
- [What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use](https://mlsanity.com/paper/1905.05134/) (2019)
- [Adversarial Machine Learning -- Industry Perspectives](https://mlsanity.com/paper/2002.05646/) (2020)
- [Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI](https://mlsanity.com/paper/1909.02309/) (2019)
- [Software Engineering for AI-Based Systems: A Survey](https://mlsanity.com/paper/2105.01984/) (2021)
- [How do Data Science Workers Collaborate? Roles, Workflows, and Tools](https://mlsanity.com/paper/2001.06684/) (2020)
- [Collaboration Challenges in Building ML-Enabled Systems: Communication, Documentation, Engineering, and Process](https://mlsanity.com/paper/2110.10234/) (2021)
- [Requirements Engineering for Machine Learning: Perspectives from Data Scientists](https://mlsanity.com/paper/1908.04674/) (2019)
- [On Testing Machine Learning Programs](https://mlsanity.com/paper/1812.02257/) (2018)
- [Software Engineering Challenges of Deep Learning](https://mlsanity.com/paper/1810.12034/) (2018)
- [Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology](https://mlsanity.com/paper/2003.05155/) (2020)
- [Assuring the Machine Learning Lifecycle: Desiderata, Methods, and Challenges](https://mlsanity.com/paper/1905.04223/) (2019)
- [Safely Entering the Deep: A Review of Verification and Validation for Machine Learning and a Challenge Elicitation in the Automotive Industry](https://mlsanity.com/paper/1812.05389/) (2018)
- [Who Needs MLOps: What Data Scientists Seek to Accomplish and How Can MLOps Help?](https://mlsanity.com/paper/2103.08942/) (2021)
- [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)
- [How AI Developers Overcome Communication Challenges in a Multidisciplinary Team: A Case Study](https://mlsanity.com/paper/2101.06098/) (2021)
- [From plane crashes to algorithmic harm: applicability of safety engineering frameworks for responsible ML](https://mlsanity.com/paper/2210.03535/) (2022)
- [An Analysis of ISO 26262: Using Machine Learning Safely in Automotive Software](https://mlsanity.com/paper/1709.02435/) (2017)
- [Operationalizing Machine Learning: An Interview Study](https://mlsanity.com/paper/2209.09125/) (2022)
- [Studying Software Engineering Patterns for Designing Machine Learning Systems](https://mlsanity.com/paper/1910.04736/) (2019)
- [Trust in Data Science: Collaboration, Translation, and Accountability in Corporate Data Science Projects](https://mlsanity.com/paper/2002.03389/) (2020)
- [Machine Learning Practices Outside Big Tech: How Resource Constraints Challenge Responsible Development](https://mlsanity.com/paper/2110.02932/) (2021)
- [Characterizing and Detecting Mismatch in Machine-Learning-Enabled Systems](https://mlsanity.com/paper/2103.14101/) (2021)

## Built on by (most cited first, 22 of 22)

- [Prompts Are Programs Too! Understanding How Developers Build Software Containing Prompts](https://mlsanity.com/paper/2409.12447/) (2024)
- [Beyond the Comfort Zone: Emerging Solutions to Overcome Challenges in Integrating LLMs into Software Products](https://mlsanity.com/paper/2410.12071/) (2024)
- [Beyond Testers' Biases: Guiding Model Testing with Knowledge Bases using LLMs](https://mlsanity.com/paper/2310.09668/) (2023)
- [A Large-Scale Study of Model Integration in ML-Enabled Software Systems](https://mlsanity.com/paper/2408.06226/) (2024)
- [Test & Evaluation Best Practices for Machine Learning-Enabled Systems](https://mlsanity.com/paper/2310.06800/) (2023)
- [How to Sustainably Monitor ML-Enabled Systems? Accuracy and Energy Efficiency Tradeoffs in Concept Drift Detection](https://mlsanity.com/paper/2404.19452/) (2024)
- [Naming the Pain in Machine Learning-Enabled Systems Engineering](https://mlsanity.com/paper/2406.04359/) (2024)
- [Innovating for Tomorrow: The Convergence of SE and Green AI](https://mlsanity.com/paper/2406.18142/) (2024)
- [A Reference Architecture of Reinforcement Learning Frameworks](https://mlsanity.com/paper/2603.06413/) (2026)
- [What About the Data? A Mapping Study on Data Engineering for AI Systems](https://mlsanity.com/paper/2402.05156/) (2024)
- [Navigating Pitfalls: Evaluating LLMs in Machine Learning Programming Education](https://mlsanity.com/paper/2505.18220/) (2025)
- [Agentic Pipelines in Embedded Software Engineering: Emerging Practices and Challenges](https://mlsanity.com/paper/2601.10220/) (2026)
- [How Do Practitioners Build SE Agents? Insights from a Mixed-Methods Study](https://mlsanity.com/paper/2607.10856/) (2026)
- [Agile Management for Machine Learning: A Systematic Mapping Study](https://mlsanity.com/paper/2506.20759/) (2025)
- [Understanding Prompt Programming Tasks and Questions](https://mlsanity.com/paper/2507.17264/) (2025)
- [Towards a Prediction of Machine Learning Training Time to Support Continuous Learning Systems Development](https://mlsanity.com/paper/2309.11226/) (2023)
- [Using Quality Attribute Scenarios for ML Model Test Case Generation](https://mlsanity.com/paper/2406.08575/) (2024)
- [Bridging the Communication Gap: Evaluating AI Labeling Practices for Trustworthy AI Development](https://mlsanity.com/paper/2501.11909/) (2025)
- [Define-ML: An Approach to Ideate Machine Learning-Enabled Systems](https://mlsanity.com/paper/2506.20621/) (2025)
- [Expanding ML-Documentation Standards For Better Security](https://mlsanity.com/paper/2507.12003/) (2025)
- [Enhancing software product lines with machine learning components](https://mlsanity.com/paper/2510.27640/) (2025)
- [Architecturally Significant MLOps Guidelines for ML Model Integration and Deployment: a Gray Literature Review](https://mlsanity.com/paper/2606.06535/) (2026)

## Abstract (arXiv)

Incorporating machine learning (ML) components into software products raises new software-engineering challenges and exacerbates existing challenges. Many researchers have invested significant effort in understanding the challenges of industry practitioners working on building products with ML components, through interviews and surveys with practitioners. With the intention to aggregate and present their collective findings, we conduct a meta-summary study: We collect 50 relevant papers that together interacted with over 4758 practitioners using guidelines for systematic literature reviews. We then collected, grouped, and organized the over 500 mentions of challenges within those papers. We highlight the most commonly reported challenges and hope this meta-summary will be a useful resource for the research community to prioritize research and education in this field.