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Monday, May 20
 

7:15am

Continental Breakfast and Badge Pickup Mezzanine

8:15am

Opening Remarks Santa Clara BallroomBharath Ramsundar • Nisha Talagala

8:30am

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

9:15am

TBA Santa Clara Ballroom

10:00am

Break with Refreshments Mezzanine

10:30am

MLOp Lifecycle Scheme for Vision-based Inspection Process in Manufacturing Track 2: Stevens Creek RoomJunsung Lim • Hoejoo Lee • Youngmin Won • Hunje Yeon Opportunities and Challenges Of Machine Learning Accelerators In Production Track 1: Winchester RoomMaheswaran Sathiamoorthy • Rajagopal Ananthanarayanan • Peter Brandt • Manasi Joshi Tutorial 1: TBA Track 3: Lawrence/San Tomas/Lafayette Rooms

10:50am

Accelerating Large Scale Deep Learning Inference through DeepCPU at Microsoft Track 1: Winchester RoomMinjia Zhang • Samyam Rajbandari • Wenhan Wang • Elton Zheng • Olatunji Ruwase • Jeff Rasley • Jason Li • Junhua Wang • Yuxiong He Deep Learning Vector Search Service Track 2: Stevens Creek RoomJeffrey Zhu • Mingqin Li

11:10am

A Distributed Machine Learning For Giant Hogweed Eradication Track 1: Winchester RoomNaoto Umemori Signal Fabric—An AI-assisted Platform for Knowledge Discovery in Dynamic System Track 2: Stevens Creek RoomSouren Aghajanyan • Roman Batoukov • Jian Zhang

11:30am

AIOps: Challenges and Experiences in Azure Track 1: Winchester RoomZe Li • Yingnong Dang Relevance Debugging and Explaining at LinkedIn Track 2: Stevens Creek RoomDaniel Qiu • Yucheng Qian Tutorial 2: TBA Track 3: Lawrence/San Tomas/Lafayette Rooms

11:50am

Shooting the Moving Target: Machine Learning in Cybersecurity Track 2: Stevens Creek RoomAnkit Arun • Ignacio Arnaldo Quasar: A High-Performance Scoring and Ranking Library Track 1: Winchester RoomAndris Birkmanis • Fei Chen

12:10pm

Deep Learning Inference Service at Microsoft Track 2: 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 Track 1: Winchester RoomP Satya Madhuri • Malini Christina Raj • Ramprakash Ramamoorthy

12:30pm

Conference Luncheon Terra Courtyard

1:30pm

Predictive Caching@Scale Track 2: Stevens Creek RoomVaishnav Janardhan • Adit Bhardwaj How the Experts Do it: Production ML at Scale Track 1: Winchester RoomJoel Young Tutorial 3: TBA Track 3: Lawrence/San Tomas/Lafayette Rooms

1:50pm

Towards Taming the Resource and Data Heterogeneity in Federated Learning Track 2: 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 Track 2: Stevens Creek RoomJack Zhang • Kai Liu

2:30pm

The Power of Metrics - How to Monitor and Improve ML Efficiency Track 2: Stevens Creek RoomYan Yan • Zhilan Zweiger Regulations, Compliance, and Biases Track 1: Winchester RoomSandeep Uttamchandani Tutorial 4: TBA Track 3: Lawrence/San Tomas/Lafayette Rooms

2:50pm

MPP: Model Performance Predictor Track 2: 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 Track 2: 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 Track 2: Stevens Creek RoomSaman Biookaghazadeh • Yitao Chen • Kaiqi Zhao • Ming Zhao Low-latency Job Scheduling with Preemption for the Development of Deep Learning Track 1: Winchester RoomHidehito Yabuuchi • Daisuke Taniwaki • Shingo Omura Tutorial 5: TBA Track 3: Lawrence/San Tomas/Lafayette Rooms

4:20pm

Continuous Training for Production ML in the TensorFlow Extended (TFX) Platform Track 2: Stevens Creek RoomDenis Baylor • Kevin Haas • Sammy Leong • Rose Liu • Clemens Menwald • Hui Miao • Neoklis Polyzotis • Martin Zinkevich • Konstantinos (Gus) Katsiapis • Mitchell Trott tensorflow-tracing: A Performance Tuning Framework for Production Track 1: Winchester RoomSayed 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 Track 1: Winchester RoomRamin Keene Reinforcement Learning Based Incremental Web Crawling Track 2: Stevens Creek RoomGaurav Tripathi

5:00pm

Bundle Data Management for Machine Learning Pipelines Track 1: Winchester RoomKenneth Yocum • Sean Rowan • Jonathan Lunt • Theodore M. Wong Katib: A Distributed General AutoML Platform Based on Kubernetes Track 2: Stevens Creek RoomJinan Zhou • Andrey Velichkevich • Kirill Prosvirov • Yuji Oshima • Anubhav Grag Tutorial 6: TBA Track 3: Lawrence/San Tomas/Lafayette Rooms

5:20pm

TonY: An Orchestrator for Distributed Machine Learning Jobs Track 1: Winchester RoomAnthony Hsu • Keqiu Hu • Jonathan Hung • Arun Suresh • Zhe Zhang Machine Learning Models as a Service Track 2: Stevens Creek RoomTobias Wenzel • Vigith Maurice

5:40pm

Stratum: A Serverless Framework for Lifecycle Management of Machine Learning based Data Analytics Tasks Track 2: Stevens Creek RoomAnirban Bhattacharjee • Yogesh Barve • Shweta Khare • Shunxing Bao • Aniruddha Gokhale • Thomas Damiano Transfer Learning for Performance Modeling of Deep Neural Network System Track 1: Winchester RoomMd Shahriar Iqbal • Lars Kotthoff • Pooyan Jamshidi

6:00pm

Happy Hour and Poster Session Mezzanine