# Design Patterns for AI-based Systems: A Multivocal Literature Review and Pattern Repository

arXiv: https://arxiv.org/abs/2303.13173 · HTML: https://mlsanity.com/paper/2303.13173/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 57 references that are in the map.
- First posted on arXiv 23 Mar 2023.

## Builds on (most cited first, 11 of 11)

- [Software Engineering for AI-Based Systems: A Survey](https://mlsanity.com/paper/2105.01984/) (2021)
- [Guidelines for including grey literature and conducting multivocal literature reviews in software engineering](https://mlsanity.com/paper/1707.02553/) (2017)
- [Engineering AI Systems: A Research Agenda](https://mlsanity.com/paper/2001.07522/) (2020)
- [Studying Software Engineering Patterns for Designing Machine Learning Systems](https://mlsanity.com/paper/1910.04736/) (2019)
- [Characterizing Technical Debt and Antipatterns in AI-Based Systems: A Systematic Mapping Study](https://mlsanity.com/paper/2103.09783/) (2021)
- [Adapting Software Architectures to Machine Learning Challenges](https://mlsanity.com/paper/2105.12422/) (2021)
- [MLOps Challenges in Multi-Organization Setup: Experiences from Two Real-World Cases](https://mlsanity.com/paper/2103.08937/) (2021)
- [Continuous Integration of Machine Learning Models with ease.ml/ci: Towards a Rigorous Yet Practical Treatment](https://mlsanity.com/paper/1903.00278/) (2019)
- [Software Engineering for Responsible AI: An Empirical Study and Operationalised Patterns](https://mlsanity.com/paper/2111.09478/) (2021)
- [Responsible-AI-by-Design: a Pattern Collection for Designing Responsible AI Systems](https://mlsanity.com/paper/2203.00905/) (2022)
- [Towards a Reference Software Architecture for Human-AI Teaming in Smart Manufacturing](https://mlsanity.com/paper/2201.04876/) (2022)

## Built on by (most cited first, 4 of 4)

- [A Synthesis of Green Architectural Tactics for ML-Enabled Systems](https://mlsanity.com/paper/2312.09610/) (2023)
- [Generating a Low-code Complete Workflow via Task Decomposition and RAG](https://mlsanity.com/paper/2412.00239/) (2024)
- [Architecturally Significant MLOps Guidelines for ML Model Integration and Deployment: a Gray Literature Review](https://mlsanity.com/paper/2606.06535/) (2026)
- [Software Engineering for AI-driven Building Operation](https://mlsanity.com/paper/2608.16237/) (2026)

## Abstract (arXiv)

Systems with artificial intelligence components, so-called AI-based systems, have gained considerable attention recently. However, many organizations have issues with achieving production readiness with such systems. As a means to improve certain software quality attributes and to address frequently occurring problems, design patterns represent proven solution blueprints. While new patterns for AI-based systems are emerging, existing patterns have also been adapted to this new context. The goal of this study is to provide an overview of design patterns for AI-based systems, both new and adapted ones. We want to collect and categorize patterns, and make them accessible for researchers and practitioners. To this end, we first performed a multivocal literature review (MLR) to collect design patterns used with AI-based systems. We then integrated the created pattern collection into a web-based pattern repository to make the patterns browsable and easy to find. As a result, we selected 51 resources (35 white and 16 gray ones), from which we extracted 70 unique patterns used for AI-based systems. Among these are 34 new patterns and 36 traditional ones that have been adapted to this context. Popular pattern categories include "architecture" (25 patterns), "deployment" (16), "implementation" (9), or "security & safety" (9). While some patterns with four or more mentions already seem established, the majority of patterns have only been mentioned once or twice (51 patterns). Our results in this emerging field can be used by researchers as a foundation for follow-up studies and by practitioners to discover relevant patterns for informing the design of AI-based systems.