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About this paper
Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro and 61 more
This position paper outlines the rationale for establishing a new interdisciplinary field, 'systems machine learning,' to address the full-stack bottlenecks in deploying ML models in real-world applications. It proposes and details the creation of the Conference on Machine Learning and Systems (MLSys) as a dedicated venue to foster research on hardware, software, and non-accuracy metrics for ML systems.
Key insights
- Real-world deployment of machine learning models is significantly hindered by 'full-stack' systems challenges that are not adequately addressed by isolated ML or systems research.
- A dedicated interdisciplinary field, 'systems machine learning,' is essential to tackle the unique requirements and bottlenecks arising from the interplay between ML algorithms and their supporting infrastructure.
- Future ML research must expand beyond solely optimizing predictive accuracy to integrate critical non-accuracy metrics such as efficiency, latency, power consumption, cost, privacy, security, and fairness into system design.
Summary by alphaXiv.
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