MLSys 2024, The Seventh Annual Conference on Machine Learning and Systems, will be held again at the Santa Clara Convention Center.
Mon May 13th through Thu the 16th
Registration
Registration is open! MLSys 2024 will be in person only, no hybrid/virtual attendance.
Pricing » Registration 2024 Registration Cancellation Policy »
Announcements
Recorded videos are up! Visit an event in the schedule to view the live recording.
Proceedings are up! See them at the following:
Careers site is open! View opportunities below:
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.
View MLSys 2024 sponsors »Become a 2025 Sponsor Sponsor Info »
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, Google Young Professionals Workshop Beidi Chen, Carnegie Mellon University Hui Guan, UMass Amherst
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The organizers can be contacted here.
Board
Tianqi Chen (President)
Alex Dimakis
Alex Smola
Ameet Talwalkar (Treasurer)
Carole Jean Wu
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. With the growing importance of holistic machine learning and systems approaches when building real-world AI systems, the MLSys conference plays an even more significant role in today’s AI landscape.
Interdisciplinary Focus: MLSys uniquely bridges the gap between machine learning and systems design. In the era of generative AI, which requires significant computational resources and innovative algorithms, this interdisciplinary approach is crucial for developing more efficient and effective AI systems.
Optimization of AI Systems: The conference discusses not just AI models but also the systems that support them. This includes topics like hardware acceleration, distributed computing, and energy-efficient designs, all of which are vital for running large-scale AI models efficiently.
Advancements in Modeling: The conference showcases the latest advancements in machine learning models with practical system considerations. With rapid developments in this field, MLSys provides a platform for researchers and practitioners to present their latest findings, contributing to the collective knowledge and progress in intelligent systems.
Industry and Academic Collaboration: MLSys is a meeting point for both industry leaders and academic researchers. This collaboration fosters the translation of academic research into practical, real-world applications in the field of machine learning and systems.
Ethical and Societal Implications: As AI systems become more prevalent, its societal and ethical implications become more significant. MLSys provides a forum for discussing these implications, ensuring that advancements in AI are aligned with ethical standards and societal needs.
Education and Training: By bringing together leading experts in the field, MLSys plays a role in education and training for the next generation of AI and systems researchers and practitioners, who will be at the forefront of developing and deploying AI technologies.
The steering committee and program committees consist of 50 leading members of the AI 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 dividends in both technology advancement and industry growth for years to come.