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DESCRIPTION:Click for Latest Location Information: http://edw2020chicago.da
 taversity.net/sessionPop.cfm?confid=143&proposalid=12310\nWhy are data anal
 ytic teams failing? There is a body of knowledge on how you manage tea
 ms successfully in technical complex systems. Those systems may be factorie
 s, software development teams, or data analytics professionals. They all fa
 il or succeed based on the same general patterns. You should view the failu
 res of data and analytic teams in the context of a century-long evolution o
 f ideas that improve how people manage complex systems. It started with pio
 neers like W. Edwards Deming, lean, and statistical process control –
  gradually these ideas crossed into the technology space in the form o
 f Agile, DevOps, and now, DataOps. Organizations eager to adopt AI and mach
 ine learning (ML) are up against significant challenges. DataOps bridges th
 e gaps between data science and operations. Our talk addresses the architec
 tural, cultural, and process considerations associated with creating a
 n agile AI/ML data analytics environment.\nDataOps is for data and analytic
  team leaders who desire to innovate, struggle to keep up with custome
 r requests, and let embarrassing data errors slip into production.&nbs
 p;DataOps architecture and process deliver new business insights by enablin
 g the development and deployment of innovative, high-quality data analytic 
 pipelines. Rapidly.\nAfter looking at trends in analytics, Gil will outline
  the steps to apply DevOps techniques from software development to cre
 ate a DataOps data architecture, including how to add tests, modulariz
 e and containerize, do branching and merging, use multiple environments, pa
 rameterize your process, use simple storage, and use multiple workflows dep
 loys to production with efficiency. He will also explain why “don&rsq
 uo;t be a hero” and “collaborate broadly” should be the m
 otto of analytic teams – emphasizing that, while being a hero can fee
 l good, it is not the path to success for individuals in analytic teams.\n
DTSTART:20201020T145500
SUMMARY:DataOps Data Architecture and AI Best Practices
DTEND:20201020T154459
LOCATION: See Description
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