# Software Architecture for ML-based Systems: What Exists and What Lies Ahead

arXiv: https://arxiv.org/abs/2103.07950 · HTML: https://mlsanity.com/paper/2103.07950/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 38 references that are in the map.
- First posted on arXiv 14 Mar 2021.

## Builds on (most cited first, 5 of 5)

- [Engineering AI Systems: A Research Agenda](https://mlsanity.com/paper/2001.07522/) (2020)
- [Robustness Guarantees for Deep Neural Networks on Videos](https://mlsanity.com/paper/1907.00098/) (2019)
- [Machine Learning Meets Quantitative Planning: Enabling Self-Adaptation in Autonomous Robots](https://mlsanity.com/paper/1903.03920/) (2019)
- [Losing Confidence in Quality: Unspoken Evolution of Computer Vision Services](https://mlsanity.com/paper/1906.07328/) (2019)
- [Beware the evolving 'intelligent' web service! An integration architecture tactic to guard AI-first components](https://mlsanity.com/paper/2005.13186/) (2020)

## Built on by (most cited first, 13 of 13)

- [Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineering](https://mlsanity.com/paper/2209.04963/) (2022)
- [A Synthesis of Green Architectural Tactics for ML-Enabled Systems](https://mlsanity.com/paper/2312.09610/) (2023)
- [Software Engineering for Responsible AI: An Empirical Study and Operationalised Patterns](https://mlsanity.com/paper/2111.09478/) (2021)
- [Towards Self-Adaptive Machine Learning-Enabled Systems Through QoS-Aware Model Switching](https://mlsanity.com/paper/2308.09960/) (2023)
- [Maintainability Challenges in ML: A Systematic Literature Review](https://mlsanity.com/paper/2408.09196/) (2024)
- [The Vision of Self-Evolving Computing Systems](https://mlsanity.com/paper/2204.06825/) (2022)
- [A Compositional Approach to Creating Architecture Frameworks with an Application to Distributed AI Systems](https://mlsanity.com/paper/2212.13570/) (2022)
- [Sequential Recommendation Model for Next Purchase Prediction](https://mlsanity.com/paper/2207.06225/) (2022)
- [Machine Learning Systems: A Survey from a Data-Oriented Perspective](https://mlsanity.com/paper/2302.04810/) (2023)
- [Modeling Quality and Machine Learning Pipelines through Extended Feature Models](https://mlsanity.com/paper/2207.07528/) (2022)
- [LoCoML: A Framework for Real-World ML Inference Pipelines](https://mlsanity.com/paper/2501.14165/) (2025)
- [Enhancing Architecture Frameworks by Including Modern Stakeholders and their Views/Viewpoints](https://mlsanity.com/paper/2308.05239/) (2023)
- [X-SYS: A Reference Architecture for Interactive Explanation Systems](https://mlsanity.com/paper/2602.12748/) (2026)

## Abstract (arXiv)

The increasing usage of machine learning (ML) coupled with the software architectural challenges of the modern era has resulted in two broad research areas: i) software architecture for ML-based systems, which focuses on developing architectural techniques for better developing ML-based software systems, and ii) ML for software architectures, which focuses on developing ML techniques to better architect traditional software systems. In this work, we focus on the former side of the spectrum with a goal to highlight the different architecting practices that exist in the current scenario for architecting ML-based software systems. We identify four key areas of software architecture that need the attention of both the ML and software practitioners to better define a standard set of practices for architecting ML-based software systems. We base these areas in light of our experience in architecting an ML-based software system for solving queuing challenges in one of the largest museums in Italy.