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UID:DE20645C-4FA0-4B17-A97C-867D1E4DD343
DTSTART;TZID=America/New_York:20250501T120000
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DESCRIPTION:Often\, the final step in the machine learning solution deploym
 ent step goes unfinished: monitoring. Data monitoring helps ensure data in
 tegrity for your applications and dashboards by identifying data drift and
  gaining insight into data changes. Model monitoring helps identify change
 s in model accuracy which can result from\, e.g.\, changes in customer beh
 avior as well as upstream data quality.\n\nIn this session\, you'll learn 
 how to detect issues early and take corrective action before these issues 
 have a significant impact\, explore the monitoring problem\, where it fits
  in the ML process\, and how to use no-code data and model monitoring user
  interfaces in Autonomous Database. Join us to learn about this key MLOps 
 topic along with demonstrations.\n\nPresenter:\n\nMark Hornick\, Senior Di
 rector\, Oracle Machine Learning and AI Product Management\n\nDate: Thursd
 ay\, May 1\, 2025 | 9:00am Pacific / 12:00pm Eastern\n\nSpeaker(s): Mark H
 ornick\, \n\nAgenda: \nhttps://us02web.zoom.us/webinar/register/4017283227
 834/WN_u_aGl58mS5C-qsWlUj0gQg#/registration\n\nPlease register in above li
 nk and here\, too. We appreciate that for the visibility of our talks.\n\n
 Virtual: https://events.vtools.ieee.org/m/483320
LOCATION:Virtual: https://events.vtools.ieee.org/m/483320
ORGANIZER:baris.kazar@oracle.com
SEQUENCE:15
SUMMARY:This Thursday: AnD TechCast | Data & Model Monitoring in Machine Le
 arning: The Step Not To Skip (AI Talks with Coffee/Tea #19)
URL;VALUE=URI:https://events.vtools.ieee.org/m/483320
X-ALT-DESC:Description: <br /><p><span style="background-color: rgb(241\, 1
 96\, 15)\;"><strong>Often\, the final step in the machine learning solutio
 n deployment step goes unfinished: monitoring. Data monitoring helps ensur
 e data integrity for your applications and dashboards by identifying data 
 drift and gaining insight into data changes. Model monitoring helps identi
 fy changes in model accuracy which can result from\, e.g.\, changes in cus
 tomer behavior as well as upstream data quality.</strong></span></p>\n<p>&
 nbsp\;</p>\n<p><span style="background-color: rgb(241\, 196\, 15)\;"><stro
 ng>In this session\, you'll learn how to detect issues early and take corr
 ective action before these issues have a significant impact\, explore the 
 monitoring problem\, where it fits in the ML process\, and how to use no-c
 ode data and model monitoring user interfaces in Autonomous Database. Join
  us to learn about this key MLOps topic along with demonstrations.</strong
 ></span></p>\n<p>&nbsp\;</p>\n<p><span style="background-color: rgb(241\, 
 196\, 15)\;"><strong>Presenter:</strong></span></p>\n<p>&nbsp\;</p>\n<p><s
 pan style="background-color: rgb(241\, 196\, 15)\;"><strong>Mark Hornick\,
  Senior Director\, Oracle Machine Learning and AI Product Management</stro
 ng></span></p>\n<p>&nbsp\;</p>\n<p><span style="background-color: rgb(241\
 , 196\, 15)\;"><strong>Date: Thursday\, May 1\, 2025 | 9:00am Pacific / 12
 :00pm Eastern</strong></span></p>\n<p>&nbsp\;</p><br /><br />Agenda: <br /
 ><p>https://us02web.zoom.us/webinar/register/4017283227834/WN_u_aGl58mS5C-
 qsWlUj0gQg#/registration</p>\n<p>Please register in above link and here\, 
 too. We appreciate that for the visibility of our talks.</p>
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