BEGIN:VCALENDAR VERSION:2.0 PRODID:-//hacksw/handcal//NONSGML v1.0//EN METHOD:PUBLISH BEGIN:VEVENT DTSTAMP:20240329T064207Z DESCRIPTION:Click for Latest Location Information: http://edw2020.dataversi ty.net/sessionPop.cfm?confid=128&proposalid=11338\n
Why are data analytic teams failing? 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, and process considerations associated with creating an agile AI/ML da ta analytics environment.
\nDataOps is for data and analytic team lea ders who desire to innovate, struggle to keep up with customer request s, and let embarrassing data errors slip into production. DataOps architecture and process deliver new business insights by enabling the dev elopment and deployment of innovative, high-quality data analytic pipelines . Rapidly.
\nAfter looking at trends in analytics, Gil will outline&n bsp;the steps to apply DevOps techniques from software development to creat e a 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 “don&rsquo ;t be a hero” and “collaborate broadly” should be the mot to of analytic teams – emphasizing that, while being a hero can feel good, it is not the path to success for individuals in analytic teams.
\ n DTSTART:20200324T154500 SUMMARY:DataOps Data Architecture and AI Best Practices DTEND:20200324T164459 LOCATION: See Description END:VEVENT END:VCALENDAR