MLSys: The New Frontier of Machine Learning Systems
By Alexander J. Ratner, Dan Alistarh, Gustavo Alonso, D. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Jennifer Tour Chayes, E. Chung and 59 more. 2019
25 citations recorded by the source corpus.
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.
Subfields: Information Systems, Information Systems and Management, Management Information Systems
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In Information Systems
In Information Systems and Management
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Citations in the corpus
- The Fallacy of AI Functionality (2022, background): “Also, as functionality is a safety risk aligned with profit-oriented goals, many model logging and evaluation operations toolsare available for organizations to leverage in the internal inspection of their systems – including tools for more continuous monitoring of deployed systems [154, 169].”