# Machine Learning Systems: A Survey from a Data-Oriented Perspective

arXiv: https://arxiv.org/abs/2302.04810 · HTML: https://mlsanity.com/paper/2302.04810/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 145 references that are in the map.
- First posted on arXiv 9 Feb 2023.

## Builds on (most cited first, 29 of 29)

- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [Green AI](https://mlsanity.com/paper/1907.10597/) (2019)
- [Qualitatively characterizing neural network optimization problems](https://mlsanity.com/paper/1412.6544/) (2014)
- [Challenges in Deploying Machine Learning: a Survey of Case Studies](https://mlsanity.com/paper/2011.09926/) (2020)
- [Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges](https://mlsanity.com/paper/2107.05847/) (2021)
- [Hyperparameter Selection for Offline Reinforcement Learning](https://mlsanity.com/paper/2007.09055/) (2020)
- [Data-Driven Grasp Synthesis - A Survey](https://mlsanity.com/paper/1309.2660/) (2013)
- [Causal Bayesian Optimization](https://mlsanity.com/paper/2005.11741/) (2020)
- [A Software Engineering Perspective on Engineering Machine Learning Systems: State of the Art and Challenges](https://mlsanity.com/paper/2012.07919/) (2020)
- [Emulation of physical processes with Emukit](https://mlsanity.com/paper/2110.13293/) (2021)
- [Automatic Discovery of Privacy-Utility Pareto Fronts](https://mlsanity.com/paper/1905.10862/) (2019)
- [The STRANDS Project: Long-Term Autonomy in Everyday Environments](https://mlsanity.com/paper/1604.04384/) (2016)
- [Software Architecture for ML-based Systems: What Exists and What Lies Ahead](https://mlsanity.com/paper/2103.07950/) (2021)
- [Data Engineering for Data Analytics: A Classification of the Issues, and Case Studies](https://mlsanity.com/paper/2004.12929/) (2020)
- [Automated Machine Learning -- a brief review at the end of the early years](https://mlsanity.com/paper/2008.08516/) (2020)
- [Crowdbreaks: Tracking Health Trends using Public Social Media Data and Crowdsourcing](https://mlsanity.com/paper/1805.05491/) (2018)
- [Offline Model Guard: Secure and Private ML on Mobile Devices](https://mlsanity.com/paper/2007.02351/) (2020)
- [Lbl2Vec: An Embedding-Based Approach for Unsupervised Document Retrieval on Predefined Topics](https://mlsanity.com/paper/2210.06023/) (2022)
- [BoGraph: Structured Bayesian Optimization From Logs for Expensive Systems with Many Parameters](https://mlsanity.com/paper/2112.08774/) (2021)
- [An Empirical Evaluation of Flow Based Programming in the Machine Learning Deployment Context](https://mlsanity.com/paper/2204.12781/) (2022)
- [Desiderata for next generation of ML model serving](https://mlsanity.com/paper/2210.14665/) (2022)
- [BigDL: A Distributed Deep Learning Framework for Big Data](https://mlsanity.com/paper/1804.05839/) (2018)
- [iTeleScope: Intelligent Video Telemetry and Classification in Real-Time using Software Defined Networking](https://mlsanity.com/paper/1804.09914/) (2018)
- [The RFML Ecosystem: A Look at the Unique Challenges of Applying Deep Learning to Radio Frequency Applications](https://mlsanity.com/paper/2010.00432/) (2020)
- [A Portable, Self-Contained Neuroprosthetic Hand with Deep Learning-Based Finger Control](https://mlsanity.com/paper/2103.13452/) (2021)
- [A Pipeline for Vision-Based On-Orbit Proximity Operations Using Deep Learning and Synthetic Imagery](https://mlsanity.com/paper/2101.05661/) (2021)
- [Resource Provisioning and Profit Maximization for Transcoding in Information Centric Networking](https://mlsanity.com/paper/1605.05758/) (2016)
- [Software Engineering for Intelligent and Autonomous Systems: Report from the GI Dagstuhl Seminar 18343](https://mlsanity.com/paper/1904.01518/) (2019)

## Built on by (most cited first, 3 of 3)

- [Can We Predict Before Executing Machine Learning Agents?](https://mlsanity.com/paper/2601.05930/) (2026)
- [Self-sustaining Software Systems (S4): Towards Improved Interpretability and Adaptation](https://mlsanity.com/paper/2401.11370/) (2024)
- [The Systems Engineering Approach in Times of Large Language Models](https://mlsanity.com/paper/2411.09050/) (2024)

## Abstract (arXiv)

Engineers are deploying ML models as parts of real-world systems with the upsurge of AI technologies. Real-world environments challenge the deployment of such systems because these environments produce large amounts of heterogeneous data, and users require increasingly efficient responses. These requirements push prevalent software architectures to the limit when deploying ML-based systems. Data-oriented Architecture (DOA) is an emerging style that equips systems better for integrating ML models. Even though papers on deployed ML systems do not mention DOA, their authors made design decisions that implicitly follow DOA. Implicit decisions create a knowledge gap, limiting the practitioners' ability to implement ML-based systems. hlbThis paper surveys why, how, and to what extent practitioners have adopted DOA to implement and deploy ML-based systems. We overcome the knowledge gap by answering these questions and explicitly showing the design decisions and practices behind these systems. The survey follows a well-known systematic and semi-automated methodology for reviewing papers in software engineering. The majority of reviewed works partially adopt DOA. Such an adoption enables systems to address requirements such as Big Data management, low latency processing, resource management, security and privacy. Based on these findings, we formulate practical advice to facilitate the deployment of ML-based systems.