PPT-Clipper: A Low Latency Online Prediction Serving System

Shreya The Problem Machine learning requires real time accurate and robust predictions under heavy query load Most machine learning frameworks care about optimizing model training not deployment. Dealer is not authorized to request deposits from Buyer in excess of the deposit required by RHC Any deposit in excess of the deposit required by RHC mu st be immediately forwarded to RHC EXTERIOR TRIM AND INTERIOR COLORS Use Paint Chip from the Ro Brian F. Cooper, Adam Silberstein, Erwin Tam, . Raghu. . Ramakrishnan. , Russell Sears. Yahoo! . Research. Presenter Duncan. Benchmarking Cloud Serving Systems with YCSB. Benchmarking . vs. Testing. K. . Qureshi. ECE, Georgia Tech. Gabriel H. Loh, AMD. Fundamental Latency Trade-offs. in Architecting DRAM Caches. MICRO 2012. 3-D Memory Stacking. 3-D Stacked memory can provide large caches at high . Elad. . Hazan. (. Technion. ). Satyen Kale . (Yahoo! Labs). Shai. . Shalev-Shwartz. (Hebrew University). Three Prediction Problems: . I. Online Collaborative Filtering. Users: . {1, 2, …, m}. Movies: . Saehoon Kim. §. , . Yuxiong He. *. ,. . Seung-won Hwang. §. , . Sameh Elnikety. *. , . Seungjin Choi. §. §. *. Web Search Engine . Requirement. 2. Queries. High quality + Low latency. This talk focuses on how to achieve low latency without compromising the quality. : . with an example application. Ming Ouhyoung, Professor. Dept. of CSIE, National Taiwan University. . 2016/11/9. . at . Mediatech. Inc. . Outline. 1. VR/AR is just going through a . Cambrian explosion. K. . Qureshi. ECE, Georgia Tech. Gabriel H. Loh, AMD. Fundamental Latency Trade-offs. in Architecting DRAM Caches. MICRO 2012. 3-D Memory Stacking. 3-D Stacked memory can provide large caches at high . at Continuous 1 ms Resolution. Weixin Wu, Yujie Dong, Adam Hoover. Dept. Electrical and Computer Engineering,. Clemson University. What is system latency. Delay from when an event is sensed to when the computer “does something” (actuates). TensorFlow. ). On a single CPU/GPU, within a data center or across data centers. Techniques used. Data Parallelism. Model Parallelism. Parameter Server. Goals: Fast training, accuracy. Gaia. 1. Inference (. Niangjun Chen . Joint work with Anish Agarwal, Lachlan Andrew, . Siddharth. Barman, and Adam Wierman. 1.  .  .  .  . 2.  .  .  .  .  .  . 3.  .  .  .  .  .  .  .  . online.  . switching cost. Wolfram Schneider ~ John Mock ~ Renee McLaughlin. for. Benefits for User. Smartphone management of the Clipper card. One less plastic card in wallet. Prevent searching for (misplaced or lost) plastic clipper card. : . with an example application. Ming Ouhyoung, Professor. Dept. of CSIE, National Taiwan University. . 2016/11/9. . at . Mediatech. Inc. . Outline. 1. VR/AR is just going through a . Cambrian explosion. Saehoon Kim. §. , . Yuxiong He. *. ,. . Seung-won Hwang. §. , . Sameh Elnikety. *. , . Seungjin Choi. §. §. *. Web Search Engine . Requirement. 2. Queries. High quality + Low latency. This talk focuses on how to achieve low latency without compromising the quality. Compensation/Mitigation. Techniques. Latency . Compensation and . Cheating. Latency and how to Compensate. What . is Latency Compensation?. Latency . compensation . is the method of . hiding. . network latency .