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DESCRIPTION:Click for Latest Location Information: http://edw2020.dataversi
 ty.net/sessionPop.cfm?confid=128&proposalid=11338\n<p>Why are data analytic
  teams failing?&nbsp;There is a body of knowledge on how you manage teams s
 uccessfully in technical complex systems. Those systems may be factories, s
 oftware development teams, or data analytics professionals. They all fail o
 r succeed based on the same general patterns. You should view the failures 
 of data and analytic teams in the context of a century-long evolution of id
 eas that improve how people manage complex systems. It started with pioneer
 s like W. Edwards Deming, lean, and statistical process control - gradually
  these ideas crossed into the technology space in the form of Agile, DevOps
  and now, DataOps. Organizations eager to adopt AI and machine learning (ML
 ) are up against significant challenges. DataOps bridges the gaps between d
 ata science and operations. Our talk addresses the architectural, cultural,
 &nbsp;and process considerations associated with creating an agile AI/ML da
 ta analytics environment.</p>\n<p>DataOps is for data and analytic team lea
 ders who desire to innovate,&nbsp;struggle to keep up with customer request
 s,&nbsp;and let embarrassing data errors slip into production.&nbsp;DataOps
  architecture and process deliver new business insights by enabling the dev
 elopment and deployment of innovative, high-quality data analytic pipelines
 . Rapidly.</p>\n<p>After looking at trends in analytics, Gil will outline&n
 bsp;the steps to apply DevOps techniques from software development to creat
 e a&nbsp;DataOps data architecture, including how to add tests, modularize 
 and containerize, do branching and merging, use multiple environments, para
 meterize your process, use simple storage, and use multiple workflows deplo
 ys to production with efficiency. He will also explain why &ldquo;don&rsquo
 ;t be a hero&rdquo; and &ldquo;collaborate broadly&rdquo; should be the mot
 to of analytic teams &ndash; emphasizing that, while being a hero can feel 
 good, it is not the path to success for individuals in analytic teams.</p>\
 n
DTSTART:20200324T154500
SUMMARY:DataOps Data Architecture and AI Best Practices
DTEND:20200324T164459
LOCATION: See Description
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