Large-Scale Machine Learning at Verizon: Theory and Applications

被引：0
|
作者：
Srivastava, Ashok [1 ]
机构：
[1] Verizon, Palo Alto, CA 94301 USA
来源：
KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING | 2016年
关键词：
Large-scale machine learning; Orion; Revenue Generation;
D O I：
10.1145/2939672.2945361
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This talk will cover recent innovations in large-scale machine learning and their applications on massive, real-world data sets at Verizon. These applications power new revenue generating products and services for the company and are hosted on a massive computing and storage platform known as Orion. We will discuss the architecture of Orion and the underlying algorithmic framework. We will also cover some of the real world aspects of building a new organization dedicated to creating new product lines based on data science.
引用
收藏
页码：417 / 417
页数：1
相关论文
共 50 条
  • [31] Angel: a new large-scale machine learning system
    Jie Jiang
    Lele Yu
    Jiawei Jiang
    Yuhong Liu
    Bin Cui
    NationalScienceReview, 2018, 5 (02) : 216 - 236
  • [32] Applications of large-scale density functional theory in biology
    Cole, Daniel J.
    Hine, Nicholas D. M.
    JOURNAL OF PHYSICS-CONDENSED MATTER, 2016, 28 (39)
  • [33] Introduction to the Special Issue on Machine Learning Methods and Applications in Large-Scale Route Planning Problems
    Winkenbach, Matthias
    Spinler, Stefan
    Pachon, Julian
    Konduri, Karthik
    TRANSPORTATION SCIENCE, 2024, 58 (01) : 1 - 7
  • [34] Consensus-Based Distributed Optimization: Practical Issues and Applications in Large-Scale Machine Learning
    Tsianos, Konstantinos I.
    Lawlor, Sean
    Rabbat, Michael G.
    2012 50TH ANNUAL ALLERTON CONFERENCE ON COMMUNICATION, CONTROL, AND COMPUTING (ALLERTON), 2012, : 1543 - 1550
  • [35] Actor-Based Incremental Tree Data Processing for Large-Scale Machine Learning Applications
    Sakurai, Kouhei
    Shimizu, Taiki
    PROCEEDINGS OF THE 9TH ACM SIGPLAN INTERNATIONAL WORKSHOP ON PROGRAMMING BASED ON ACTORS, AGENTS, AND DECENTRALIZED CONTROL (AGERE '19), 2019, : 1 - 10
  • [36] Towards an Optimized GROUP BY Abstraction for Large-Scale Machine Learning
    Li, Side
    Kumar, Arun
    PROCEEDINGS OF THE VLDB ENDOWMENT, 2021, 14 (11): : 2327 - 2340
  • [37] Lotus: A New Topology for Large-scale Distributed Machine Learning
    Lu, Yunfeng
    Gu, Huaxi
    Yu, Xiaoshan
    Chakrabarty, Krishnendu
    ACM JOURNAL ON EMERGING TECHNOLOGIES IN COMPUTING SYSTEMS, 2021, 17 (01)
  • [38] Toward Large-Scale Vulnerability Discovery using Machine Learning
    Grieco, Gustavo
    Grinblat, Guillermo Luis
    Uzal, Lucas
    Rawat, Sanjay
    Feist, Josselin
    Mounier, Laurent
    CODASPY'16: PROCEEDINGS OF THE SIXTH ACM CONFERENCE ON DATA AND APPLICATION SECURITY AND PRIVACY, 2016, : 85 - 96
  • [39] Conformal Prediction in Spark: Large-Scale Machine Learning with Confidence
    Capuccini, Marco
    Carlsson, Lars
    Norinder, Ulf
    Spjuth, Ola
    2015 IEEE/ACM 2ND INTERNATIONAL SYMPOSIUM ON BIG DATA COMPUTING (BDC), 2015, : 61 - 67
  • [40] Security of NVMe Offloaded Data in Large-Scale Machine Learning
    Krauss, Torsten
    Goetz, Raphael
    Dmitrienko, Alexandra
    COMPUTER SECURITY - ESORICS 2023, PT IV, 2024, 14347 : 143 - 163
12345