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ML Systems Workshop @ NIPS 2017
Aparna Lakshmiratan · Sarah Bird · Siddhartha Sen · Christopher Ré · Li Erran Li · Joseph Gonzalez · Daniel Crankshaw

Fri Dec 08 08:00 AM -- 06:30 PM (PST) @ S1
Event URL: http://learningsys.org/nips17/ »

A new area is emerging at the intersection of artificial intelligence, machine learning, and systems design. This birth is driven 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 practical large-scale ML systems. In particular, we aim to elicit new connections among these diverse fields, and identify tools, best practices and design principles. We also want to think about how to do research in this area and properly evaluate it. The workshop will cover ML and AI platforms and algorithm toolkits, 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 at one ML venue and one Systems venue, and eventually merge these communities into a full conference venue. We believe this dual approach will help to create a low barrier to participation for both communities.

Fri 8:45 a.m. - 9:00 a.m. [iCal]
Opening Remarks (Talk)
Fri 9:00 a.m. - 9:20 a.m. [iCal]
Invited Talk: Ray: A distributed execution engine for emerging AI applications, Ion Stoica, UC Berkeley (Talk)
Ion Stoica
Fri 9:20 a.m. - 9:40 a.m. [iCal]
Contributed Talk 1: The Case for Learning Database Indexes (Talk)
Fri 9:40 a.m. - 10:00 a.m. [iCal]
Invited Talk: Federated Multi-Task Learning, Virginia Smith, Stanford University (Talk)
Virginia Smith
Fri 10:00 a.m. - 10:40 a.m. [iCal]
Poster Previews: 1 min lightning talks (Talks)
Fri 11:30 a.m. - 11:50 a.m. [iCal]
Invited Talk: Accelerating Persistent Neural Networks at Datacenter Scale, Daniel Lo, Microsoft Research (Talk)
Daniel Lo
Fri 11:50 a.m. - 12:10 p.m. [iCal]
Contributed Talk 2: DLVM: A modern compiler framework for neural network DSLs (Talk)
Fri 12:10 p.m. - 1:30 p.m. [iCal]
Lunch (Break)
Fri 1:20 p.m. - 2:50 p.m. [iCal]
Updates from Current ML Systems (TensorFlow, PyTorch, Caffe2, CNTK, MXNet, TVM, Clipper, MacroBase, ModelDB) (Talk)
Rajat Monga, Soumith Chintala, Cha Zhang, Tianqi Chen, Dan Crankshaw, Kai Sheng Tai, Andrew Tulloch, Manasi Vartak
Fri 2:50 p.m. - 3:20 p.m. [iCal]
Invited Talk: Machine Learning for Systems and Systems for Machine Learning, Jeff Dean, Google Brain (Talk)
Jeff Dean
Fri 3:20 p.m. - 3:40 p.m. [iCal]
Invited Talk: Creating an Open and Flexible ecosystem for AI models with ONNX, Sarah Bird, Dmytro Dzhulgakov, Facebook Research (Talk)
Sarah Bird
Fri 3:40 p.m. - 4:30 p.m. [iCal]
Posters and Coffee (Poster Session)
Jean-Baptiste Tristan, Yunseong Lee, Anna Veronika Dorogush, Shohei Hido, Michael Terry, Mennatullah Siam, Hidemoto Nakada, Cody Coleman, Jung-Woo Ha, Hao Zhang, Adam Stooke, Chen Meng, Chris Kappler, Lane Schwartz, Christopher Olston, Sebastian Schelter, Minmin Sun, Daniel Kang, Waldemar Hummer, Jichan Chung, Tim Kraska, Kannan Ramchandran, Nick Hynes, Christoph Boden, Donghyun Kwak
Fri 4:30 p.m. - 4:50 p.m. [iCal]
Contributed Talk 3: NSML: A Machine Learning Platform That Enables You to Focus on Your Models (Talk)
Fri 4:50 p.m. - 5:10 p.m. [iCal]
Contributed Talk 4: DAWNBench: An End-to-End Deep Learning Benchmark and Competition (Talk)
Fri 5:10 p.m. - 6:15 p.m. [iCal]
Panel (Discussion Panel)
Garth Gibson, Joseph Gonzalez, John Langford, Dawn Song

Author Information

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)
Chris Ré (Stanford)
Li Erran Li (Pony.ai)

Li Erran Li is the head of machine learning at Scale and an adjunct professor at Columbia University. Previously, he was chief scientist at Pony.ai. Before that, he was with the perception team at Uber ATG and machine learning platform team at Uber where he worked on deep learning for autonomous driving, led the machine learning platform team technically, and drove strategy for company-wide artificial intelligence initiatives. He started his career at Bell Labs. Li’s current research interests are machine learning, computer vision, learning-based robotics, and their application to autonomous driving. He has a PhD from the computer science department at Cornell University. He’s an ACM Fellow and IEEE Fellow.

Joseph Gonzalez (UC Berkeley)
Dan Crankshaw (UC Berkeley RISE Lab)

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