| Tag |
First Indicator |
Second Indicator |
Subfields |
| LEADER |
00000ugm a2200000 a 4500 |
| 001 |
in00004441321 |
| 005 |
20260122203307.5 |
| 006 |
m o d |
| 007 |
cr cn |
| 008 |
021020s2019 xx o v eng |
| 020 |
|
|
|z 0636920424031
|
| 024 |
8 |
|
|a 0636920424055
|
| 035 |
|
|
|a (CaSebORM)0636920424055
|
| 040 |
|
|
|d UtOrBLW
|
| 041 |
0 |
|
|a eng
|
| 100 |
1 |
|
|a Luo, Zhenxiao,
|e author.
|
| 245 |
1 |
0 |
|a How machine learning powers the Uber infrastructure /
|c Luo, Zhenxiao.
|
| 250 |
|
|
|a 1st edition.
|
| 264 |
|
1 |
|b O'Reilly Media, Incorporated,
|c 2019.
|
| 300 |
|
|
|a 1 online resource (1 video file, approximately 42 min.)
|
| 336 |
|
|
|a two-dimensional moving image
|b tdi
|2 rdacontent
|
| 337 |
|
|
|a computer
|b c
|2 rdamedia
|
| 338 |
|
|
|a online resource
|b cr
|2 rdacarrier
|
| 347 |
|
|
|a video file
|
| 520 |
|
|
|a From determining the most convenient rider pickup points to predicting the fastest routes, Uber uses data-driven machine learning to create seamless trip experiences. Zhenxiao Luo explains how Uber tackles data caching in large-scale machine learning, exploring Uber's machine learning architecture, how Uber uses big data to power machine learning, and how to use data caching to speed up AI jobs.
|
| 533 |
|
|
|a Electronic reproduction.
|b Boston, MA :
|c Safari,
|n Available via World Wide Web.
|
| 538 |
|
|
|a Mode of access: World Wide Web.
|
| 542 |
|
|
|f Copyright © O'Reilly Media, Incorporated.
|
| 550 |
|
|
|a Made available through: Safari, an O'Reilly Media Company.
|
| 588 |
0 |
|
|a Online resource; Title from title screen (viewed May 30, 2019)
|
| 500 |
|
|
|a Videorecording.
|
| 655 |
|
7 |
|a Electronic videos.
|2 local
|
| 710 |
2 |
|
|a Safari, an O'Reilly Media Company.
|
| 856 |
4 |
0 |
|u https://proxy.library.tamu.edu/login?url=https://go.oreilly.com/TAMU/library/view/-/0636920424055/?ar
|z Connect to this electronic resource
|t 0
|
| 999 |
f |
f |
|s ac206ce4-097e-3e59-8dd2-49f56bdfe51a
|i 0bc2e2c9-60c6-349b-ae9c-860fde66af7c
|t 0
|
| 952 |
f |
f |
|a Texas A&M University
|b College Station
|c Electronic Resources
|s www_evans
|d Available Online
|t 0
|h No information provided
|
| 998 |
f |
f |
|t 0
|l Available Online
|