This workshop is part two of a two-part series with one day focusing on ML for Systems and the other on Systems for ML. Although the two workshops are being led by different organizers, we are coordinating our call for papers to ensure that the workshops complement each other and that submitted papers are routed to the appropriate venue.
The ML for Systems workshop focuses on developing ML to optimize systems while we focus on designing systems to enable large scale ML with Systems for ML. Both fields are mature enough to warrant a dedicated workshop. Organizers on both sides are open to merging in the future, but this year we plan to run them separately on two different days.
A new area is emerging at the intersection of artificial intelligence, machine learning, and systems design. This has been accelerated by the explosive growth of diverse applications of ML in production, the continued growth in data volume, and the complexity of large-scale learning systems. The goal of this workshop is to bring together experts working at the crossroads of machine learning, system design and software engineering to explore the challenges faced when building large-scale ML systems. In particular, we aim to elicit new connections among these diverse fields, identifying theory, tools and design principles tailored to practical machine learning workflows. We also want to think about best practices for research in this area and how to evaluate it. The workshop will cover state of the art ML and AI platforms and algorithm toolkits (e.g. TensorFlow, PyTorch1.0, MXNet etc.), as well as dive into machine learning-focused developments in distributed learning platforms, programming languages, data structures, GPU processing, and other topics.
This workshop will follow the successful model we have previously run at ICML, NIPS and SOSP 2017.
Our plan is to run this workshop annually co-located with one ML venue and one Systems venue, to help build a strong community which we think will complement newer conferences like SysML targeting research at the intersection of systems and machine learning. We believe this dual approach will help to create a low barrier to participation for both communities.
Aparna Lakshmiratan (Facebook)
I am the PM lead for the AI Platform in Facebook AI (PyTorch 1.0, Data Tools and Developer Ecosystem) Before Facebook, I worked in Microsoft building and shipping several products including a new Click Prediction system for Bing Ads, several enhancements to the Speller and Query Alterations engine in Bing and most recently an interactive machine learning platform for non-experts at Microsoft Research. I have a PhD in Computer Science from MIT.
Sarah Bird (Facebook AI Research)
Sarah leads research and emerging technology strategy for Azure AI. Sarah works to accelerate the adoption and impact of AI by bringing together the latest innovations research with the best of open source and product expertise to create new tools and technologies. Sarah is currently leading the development of responsible AI tools in Azure Machine Learning. She is also an active member of the Microsoft AETHER committee, where she works to develop and drive company-wide adoption of responsible AI principles, best practices, and technologies. Sarah was one of the founding researchers in the Microsoft FATE research group and prior to joining Microsoft worked on AI fairness in Facebook. Sarah is active contributor to the open source ecosystem, she co-founded ONNX, an open source standard for machine learning models and was a leader in the Pytorch 1.0 project. She was an early member of the machine learning systems research community and has been active in growing and forming the community. She co-founded the SysML research conference and the Learning Systems workshops. She has a Ph.D. in computer science from UC Berkeley advised by Dave Patterson, Krste Asanovic, and Burton Smith.
Siddhartha Sen (Microsoft Research)
Joseph Gonzalez (UC Berkeley)
Dan Crankshaw (UC Berkeley RISE Lab)
More from the Same Authors
2019 Workshop: MLSys: Workshop on Systems for ML »
Aparna Lakshmiratan · Siddhartha Sen · Joseph Gonzalez · Dan Crankshaw · Sarah Bird
2018 Workshop: Workshop on Ethical, Social and Governance Issues in AI »
Chloe Bakalar · Sarah Bird · Tiberio Caetano · Edward W Felten · Dario Garcia · Isabel Kloumann · Finnian Lattimore · Sendhil Mullainathan · D. Sculley
2017 Workshop: ML Systems Workshop @ NIPS 2017 »
Aparna Lakshmiratan · Sarah Bird · Siddhartha Sen · Christopher Ré · Li Erran Li · Joseph Gonzalez · Daniel Crankshaw
2016 Workshop: Machine Learning Systems »
Aparna Lakshmiratan · Li Erran Li · Siddhartha Sen · Sarah Bird · Hussein Mehanna
2015 Workshop: Machine Learning Systems »
Alex Beutel · Tianqi Chen · Sameer Singh · Elaine Angelino · Markus Weimer · Joseph Gonzalez
2014 Poster: Parallel Double Greedy Submodular Maximization »
Xinghao Pan · Stefanie Jegelka · Joseph Gonzalez · Joseph K Bradley · Michael Jordan
2013 Workshop: Big Learning : Advances in Algorithms and Data Management »
Xinghao Pan · Haijie Gu · Joseph Gonzalez · Sameer Singh · Yucheng Low · Joseph Hellerstein · Derek G Murray · Raghu Ramakrishnan · Michael Jordan · Christopher Ré
2013 Poster: Optimistic Concurrency Control for Distributed Unsupervised Learning »
Xinghao Pan · Joseph Gonzalez · Stefanie Jegelka · Tamara Broderick · Michael Jordan