Registration
MLSys 2024 will be in person only, no hybrid/virtual attendance. Registration details will be available shortly.
Book Your Hotel
MLSYS has proudly selected the Hilton Santa Clara as its exclusive and official hotel for the 2024 event. We would like to urge all attendees to make their hotel reservations solely through the group link below. Utilizing alternate platforms, seeking other sources, or opting for different accommodations could jeopardize our ability to meet the required minimums. By reserving your room directly through the link below, you actively contribute to safeguarding the Conference’s exceptional performance. Let’s join forces in making MLSYS 2024 an unforgettable success. Availability is on a first-come, first-available basis.
Book Now!
Announcements
Call For Papers Posted! Visit the CFP page for info.
MLSys 2024 will return to Santa Clara Convention Center! Thank you to all our attendees from previous years, we look forward to seeing you again at one of our familiar and favorite venues. Calls will be posted shortly.
Schedule
Schedule details will be available closer to the conference.
Sponsors
We would like to thank our (growing) list of sponsors for their strong support for the MLSys Community. If your company would like to sponsor, please let us know.
Become a 2024 Sponsor »(not currently taking applications)Conference Overview
The Conference on Machine Learning and Systems targets research at the intersection of machine learning and systems. The conference aims to elicit new connections amongst these fields, including identifying best practices and design principles for learning systems, as well as developing novel learning methods and theory tailored to practical machine learning workflows. Topics include:
- Efficient model training, inference, and serving
- Privacy and security for ML applications
- Fairness, interpretability and explainability for ML applications
- ML programming models and abstractions
- Programming languages for machine learning
- Specialized hardware for machine learning
- Machine Learning for Systems
- Distributed and parallel learning algorithms
- Testing, debugging, and monitoring of ML applications
- Data preparation, feature selection, and feature extraction
- ML compilers and runtime
- Visualization of data, models, and predictions
- Hardware-efficient ML methods
- Systems for Machine Learning
Organizing Committee
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General Chair Phillip Gibbons, CMU Program Chairs Gennady Pekhimenko, Toronto Sponsor Chair Wenming Ye, Amazon
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The organizers can be contacted here.
Board
Tianqi Chen (President)
Alex Dimakis
Alex Smola
Ameet Talwalkar (Treasurer)
Carole Jean
Dawn Song
Diana Marculescu
Ion Stoica
Michael Carbin
Yuejie Chi
Steering Committee
Jennifer Chayes
Bill Dally
Jeff Dean
Michael I. Jordan
Yann LeCun
Fei-Fei Li
Alex Smola
Dawn Song
Eric Xing
Mission Statement
The non-profit corporation that runs MLSys aims to foster the exchange of research advances at the intersection of machine learning and systems, principally by hosting an annual interdisciplinary academic conference with the highest ethical standards for a diverse and inclusive community.
About the Conference
The MLSys community recognized that many critical future challenges are at the intersection of Machine Learning and Systems. The community was created to solve these exciting problems by recognizing the needs for scaling interdisciplinary collaboration as well as the importance of working together between industry and academia.
The community has come together to create a new conference for research spanning ML and systems, MLSys. The goal is to make a home for exceptional new research, a venue to showcase the growing trend, and more importantly a way to scale ever more complex research collaboration.
The steering committee and program committees consist of 50 leading members of emerging machine learning systems area coming from industry and academia with expertise ranging from machine learning to systems to security. The MLSys community welcomes industry participation and sponsorships, we believe the investment will pay dividend in both technology advancement and industry growth for years to come.