# MLSys: The New Frontier of Machine Learning Systems

arXiv: https://arxiv.org/abs/1904.03257 · HTML: https://mlsanity.com/paper/1904.03257/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2019.

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

- [The Fallacy of AI Functionality](https://mlsanity.com/paper/2206.09511/) (2022)
- [Yes We Care! -- Certification for Machine Learning Methods through the Care Label Framework](https://mlsanity.com/paper/2105.10197/) (2021)
- [COVID-Net MLSys: Designing COVID-Net for the Clinical Workflow](https://mlsanity.com/paper/2109.06421/) (2021)
- [The Machine Learning Canvas: Empirical Findings on Why Strategy Matters More Than AI Code Generation](https://mlsanity.com/paper/2601.01839/) (2026)

## Abstract (arXiv)

Machine learning (ML) techniques are enjoying rapidly increasing adoption. However, designing and implementing the systems that support ML models in real-world deployments remains a significant obstacle, in large part due to the radically different development and deployment profile of modern ML methods, and the range of practical concerns that come with broader adoption. We propose to foster a new systems machine learning research community at the intersection of the traditional systems and ML communities, focused on topics such as hardware systems for ML, software systems for ML, and ML optimized for metrics beyond predictive accuracy. To do this, we describe a new conference, MLSys, that explicitly targets research at the intersection of systems and machine learning with a program committee split evenly between experts in systems and ML, and an explicit focus on topics at the intersection of the two.