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PRODID:IEEE vTools.Events//EN
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DTSTART:20260308T030000
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DTSTART:20251102T010000
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DTSTAMP:20260129T013919Z
UID:E84F1D7A-C606-42DC-BAEB-33C023A196AE
DTSTART;TZID=America/Chicago:20260114T180000
DTEND;TZID=America/Chicago:20260114T190000
DESCRIPTION:Data drift refers to changes in data characteristics when compa
 ring two distinct populations. In production systems\, such shifts can imp
 act downstream analytics and decision logic. In large-scale network enviro
 nments\, traffic patterns evolve continuously\, and automated drift monito
 ring can complement traditional operational dashboards by focusing on dist
 ributional changes in the data.\n\nThis session uses network traffic data 
 to demonstrate a practical approach for identifying and interpreting data 
 drift. The presentation shows how key traffic attributes can be tracked ov
 er time and translated into clear\, interpretable signals for analytics te
 ams.\n\nAttendees will gain insight into designing an automated monitoring
  workflow\, interpreting drift signals in an operational context\, and app
 lying these insights to support more consistent and dependable analytics b
 ehavior over time.\n\nSpeaker(s): Anil Cavale\, \n\nVirtual: https://event
 s.vtools.ieee.org/m/524412
LOCATION:Virtual: https://events.vtools.ieee.org/m/524412
ORGANIZER:arnabdas@ieee.org
SEQUENCE:74
SUMMARY:Automated Data Drift Monitoring in Production Systems Using Network
  Traffic Data
URL;VALUE=URI:https://events.vtools.ieee.org/m/524412
X-ALT-DESC:Description: <br /><p class="MsoNormal">Data drift refers to cha
 nges in data characteristics when comparing two distinct populations. In p
 roduction systems\, such shifts can impact downstream analytics and decisi
 on logic. In large-scale network environments\, traffic patterns evolve co
 ntinuously\, and automated drift monitoring can complement traditional ope
 rational dashboards by focusing on distributional changes in the data.</p>
 \n<p class="MsoNormal">This session uses network traffic data to demonstra
 te a practical approach for identifying and interpreting data drift. The p
 resentation shows how key traffic attributes can be tracked over time and 
 translated into clear\, interpretable signals for analytics teams.</p>\n<p
  class="MsoNormal">Attendees will gain insight into designing an automated
  monitoring workflow\, interpreting drift signals in an operational contex
 t\, and applying these insights to support more consistent and dependable 
 analytics behavior over time.</p>\n<p>&nbsp\;</p>
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