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Monday, May 20
 

8:00am

Continental Breakfast and Badge Pickup Mezzanine

9:00am

Opening Remarks Santa Clara BallroomBharath Ramsundar • Nisha Talagala

9:15am

Ray: A Distributed Framework for Emerging AI Applications Santa Clara BallroomMichael I. Jordan

10:00am

Break with Refreshments Mezzanine

10:30am

MLOp Lifecycle Scheme for Vision-based Inspection Process in Manufacturing Stevens Creek RoomJunsung Lim • Hoejoo Lee • Youngmin Won • Hunje Yeon Opportunities and Challenges Of Machine Learning Accelerators In Production Winchester RoomRajagopal Ananthanarayanan • Peter Brandt • Maheswaran Sathiamoorthy • Manasi Joshi Using Amazon SageMaker to Operationalize Machine Learning Lawrence/San Tomas/Lafayette RoomsKumar Venkateswar

10:50am

Accelerating Large Scale Deep Learning Inference through DeepCPU at Microsoft Winchester RoomMinjia Zhang • Samyam Rajbandari • Wenhan Wang • Elton Zheng • Olatunji Ruwase • Jeff Rasley • Jason Li • Junhua Wang • Yuxiong He Deep Learning Vector Search Service Stevens Creek RoomMingqin Li • Jeffrey Zhu

11:10am

A Distributed Machine Learning For Giant Hogweed Eradication Winchester RoomMasaru Dobashi • Naoto Umemori Signal Fabric—An AI-assisted Platform for Knowledge Discovery in Dynamic System Stevens Creek RoomSouren Aghajanyan • Roman Batoukov • Jian Zhang

11:30am

AIOps: Challenges and Experiences in Azure Winchester RoomZe Li • Yingnong Dang Relevance Debugging and Explaining at LinkedIn Stevens Creek RoomDaniel Qiu • Yucheng Qian Nvidia “NGC” Deep Learning Containers Lawrence/San Tomas/Lafayette RoomsChetan Tekur • Fraser Gardiner

11:50am

Shooting the Moving Target: Machine Learning in Cybersecurity Stevens Creek RoomAnkit Arun • Ignacio Arnaldo Quasar: A High-Performance Scoring and Ranking Library Winchester RoomAndris Birkmanis • Fei Chen

12:10pm

Deep Learning Inference Service at Microsoft Stevens Creek RoomJonathan Soifer • Jason Li • Mingqin Li • Jeffrey Zhu • Yingnan Li • Yuxiong He • Elton Zheng • Adi Oltean • Maya Mosyak • Chris Barnes • Thomas Liu • Junhua Wang AI from Labs to Production - Challenges and Learnings Winchester RoomP Satya Madhuri • Malini Christina Raj • Ramprakash Ramamoorthy

12:30pm

Conference Luncheon Magnolia Room

1:30pm

Predictive Caching@Scale Stevens Creek RoomVaishnav Janardhan • Adit Bhardwaj How the Experts Do it: Production ML at Scale Lawrence/San Tomas/Lafayette RoomsJoel Young • Sandhya Ramu • Andrew Hoh • Aditya Kalro • Faisal Siddiqi • Pranav Khaitan Deep Learning Lifecycle Management with Kubernetes, REST, and Python Winchester RoomBoris Tvaroska

1:50pm

Towards Taming the Resource and Data Heterogeneity in Federated Learning Stevens Creek RoomZheng Chai • Hannan Fayyaz • Zeshan Fayyaz • Ali Anwar • Yi Zhou • Nathalie Baracaldo • Heiko Ludwig • Yue Cheng

2:10pm

A Comprehensive View of a Large-Scale Deep Learning Offline Platform Stevens Creek RoomLukas Majercak • Shansi Ren • Jack Zhang • Junhua Wang

2:30pm

The Power of Metrics—How to Monitor and Improve ML Efficiency Stevens Creek RoomYan Yan • Zhilan Zweiger Impact of Data Regulations and Bias on Operational ML Lawrence/San Tomas/Lafayette RoomsSandeep Uttamchandani • Kapil Surlaker • Sean Grullon • Sendil Thangavelu • Arthur Roberts ModelOps on AWS Winchester RoomJohn Calhoun

2:50pm

MPP: Model Performance Predictor Stevens Creek RoomSindhu Ghanta • Sriram Subramanian • Lior Khermosh • Harshil Shah • Yakov Goldberg • Swaminathan Sundararaman • Drew Roselli • Nisha Talagala

3:10pm

Manifold: A Model-Agnostic Visual Debugging Tool for Machine Learning at Uber Stevens Creek RoomLezhi Li • Yunfeng Bai • Yang Wang

3:30pm

Break with Refreshements Mezzanine

4:00pm

KnowledgeNet: Disaggregated and Distributed Training and Serving of Deep Neural Networks Stevens Creek RoomSaman Biookaghazadeh • Yitao Chen • Kaiqi Zhao • Ming Zhao Low-latency Job Scheduling with Preemption for the Development of Deep Learning Lawrence/San Tomas/Lafayette RoomsHidehito Yabuuchi • Daisuke Taniwaki • Shingo Omura Accelerating the Machine Learning Lifecycle with MLflow Winchester RoomAndrew Chen

4:20pm

Continuous Training for Production ML in the TensorFlow Extended (TFX) Platform Stevens Creek RoomDenis Baylor • Kevin Haas • Konstantinos (Gus) Katsiapis • Sammy Leong • Rose Liu • Clemens Menwald • Hui Miao • Neoklis Polyzotis • Mitchell Trott • Martin Zinkevich tensorflow-tracing: A Performance Tuning Framework for Production Lawrence/San Tomas/Lafayette RoomsSayed Hadi Hashemi • Paul Rausch • Benjamin Rabe • Kuan-Yen Chou • Simeng Liu • Volodymyr Kindratenko • Roy H Campbell

4:40pm

Fast, Reliable, Yet Catastrophically Failing!?! Safely Avoiding Incidents When Putting Machine Learning into Production Lawrence/San Tomas/Lafayette RoomsRamin Keene Reinforcement Learning Based Incremental Web Crawling Stevens Creek RoomVatsal Agarwal

5:00pm

Disdat: Bundle Data Management for Machine Learning Pipelines Lawrence/San Tomas/Lafayette RoomsSean Rowan • Jonathan Lunt • Theodore M. Wong • Ken Yocum Katib: A Distributed General AutoML Platform Based on Kubernetes Stevens Creek RoomJinan Zhou • Andrey Velichkevich • Kirill Prosvirov • Anubhav Grag • Yuji Oshima • Debo Dutta Consistent Multi-Cloud AI Lifecycle Management with Kubeflow Winchester RoomDebo Dutta • Xinyuan Huang

5:20pm

TonY: An Orchestrator for Distributed Machine Learning Jobs Lawrence/San Tomas/Lafayette RoomsAnthony Hsu • Keqiu Hu • Jonathan Hung • Arun Suresh • Zhe Zhang Machine Learning Models as a Service Stevens Creek RoomTobias Wenzel • Vigith Maurice

5:40pm

Stratum: A Serverless Framework for Lifecycle Management of Machine Learning based Data Analytics Tasks Stevens Creek RoomAnirban Bhattacharjee • Yogesh Barve • Shweta Khare • Shunxing Bao • Aniruddha Gokhale • Thomas Damiano Transfer Learning for Performance Modeling of Deep Neural Network System Lawrence/San Tomas/Lafayette RoomsMd Shahriar Iqbal • Lars Kotthoff • Pooyan Jamshidi

6:00pm

Happy Hour and Poster Session Mezzanine