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DTSTAMP:20260511T023322Z
DESCRIPTION:Click for Latest Location Information: http://edw2018.dataversi
 ty.net/sessionPop.cfm?confid=121&proposalid=9706\n<p>Many organizations hav
 e interest or initiatives in Machine Learning (ML) and Artificial Intellige
 nce (AI) projects. There are certainly challenges in algorithm and model cr
 eation; however, the models produced need a continuous flow of information 
 to learn over time. This presentation gives an overview of how we successfu
 lly structured our environment to support a sizeable strategic project, in 
 concert with bringing additional value to our organization by implementing 
 data as a service through data virtualization. Some topics we will address 
 are:&nbsp;</p>\n\n	How do I continuously acquire and deploy data?&nbsp;\n
 How do I manage big data effectively?\n
 How do I ensure models can continuously learn?&nbsp;\n
 How do I position&nbsp;to effectively exploit future data sources?&nbsp;\n
 How do I handle data protection while working with an external research par
 tner?\n
 How do I position for reuse of information for self-service and analytics?\
 n
 What are some challenges and solutions associated with AI and ML projects?\
 n\n
DTSTART:20180424T133000
SUMMARY:Data Architecture for Machine Learning
DTEND:20180424T142959
LOCATION: See Description
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