Description and Motivation
The broadening use of machine learning, the explosive growth in data, and the complexity of the large-scale learning systems required to analyze these data have together fueled interdisciplinary research at the intersection of Machine Learning and System design. Addressing these challenges demands a combination of the right abstractions -- for algorithms, data structures, and interfaces -- as well as scalable systems capable of addressing real world learning problems. At the same time, it is becoming increasingly clear that data-driven and learning-driven approaches provide natural and powerful solutions to building and managing complex modern systems. In total, the flow of ideas between these two communities continues to offer promising opportunities toward solving even larger problems.
Designing systems for machine learning presents new challenges and opportunities over the design of traditional data processing systems. For example, what is the right abstraction for data consistency in the context of parallel, stochastic learning algorithms? What guarantees of fault tolerance are needed during distributed learning? The statistical nature of machine learning offers an opportunity for more efficient systems but requires revisiting many of the challenges addressed by the systems and database communities over the past few decades. Machine learning focused developments in distributed learning platforms, programming languages, data structures, general purpose GPU programming, and a wide variety of other domains have had and will continue to have a large impact in both academia and industry.
As the relationship between the machine learning and systems communities has grown stronger, new research in using machine learning tools to solve classic systems challenges has also grown. Specifically, as we develop larger and more complex systems and networks for storing, analyzing, serving, and interacting with data, machine learning offers promise for modeling system dynamics, detecting issues, and making intelligent, data-driven decisions within our systems. Machine learning techniques have begun to play critical roles in scheduling, system tuning, and network analysis. Through working with systems and databases researchers to solve systems challenges, machine learning researchers can both improve their own learning systems as well as impact the systems community and infrastructure at large.
This is a successor to the Big Learning workshop, which in past NIPS successfully focused on and brought attention to the need for scaling machine learning. Moving forward, this Machine Learning Systems workshop aims to address research at the intersection of machine learning and systems.
Speakers
- Jeff Dean, Google [Video]
- Alex Smola, Carnegie Mellon University and Marianas Labs [Video]
- Joseph Gonzalez, University of California, Berkeley [Video]
- Chris Re, Stanford University [Video]
- Sarah Bird, Microsoft Research [Video]
- Mathias Brandewinder, Clear Lines Consulting [Video]
- Roxana Geambasu, Columbia University [Video]
Important Dates
- Submission Deadline: October 24, 2015
- Notification: November 2, 2015
- Workshop: December 12, 2015
Organizers
Theme of the Workshop
This workshop aims to bring together researchers from the machine learning and systems communities. We invite high-quality extended abstracts of original research addressing two major questions: (1) How should we design useful abstractions and build scalable systems to support large-scale machine learning? (2) How can we use machine learning in our systems to make them smarter and more efficient? Focal points for discussions and solicited submissions include but are not limited to:
- Systems for online and batch learning algorithms
- Systems for out-of-core machine learning
- Systems for scalable deep learning
- Implementation studies of large-scale distributed learning algorithms --- challenges faced and lessons learned
- Database systems for Big Learning --- models and algorithms implemented, properties (fault tolerance, consistency, scalability, etc.), strengths and limitations
- Programming languages for machine learning
- Data driven systems --- learning for job scheduling, configuration tuning, straggler mitigation, network configuration, security, and other systems challenges
- Systems for interactive machine learning
- Systems for serving machine learning models at scale