Real-time Big Data Analytics: From Deployment to Production

DDBD Ballroom CD
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Taking data science into action requires deploying statistical models into production environments, usually with real-time processing requirements. Every company that relies on predictive models to drive their applications and operations has a different process for model deployment, but by working with many such companies I’ve seen a common pattern emerge. The real-time model deployment process can be broken down into these five stages:

  • Data distillation
  • Model development
  • Model validation and deployment
  • Model refresh
  • Real-time model scoring

In this talk, I’ll describe the five stages of real-time analytics deployment, and the technologies supporting each stage, including Hadoop, Python, R, and database warehousing systems. I’ll share some best practices for setting up a the technology stack and processes for model deployment, based on some real-life case studies.

Photo of David Smith (CDA)

David Smith (CDA)


David Smith is the Vice President of Marketing and Community at Revolution Analytics, the leading provider of software and services for the open-source R statistical language. David writes daily about applications of R, analytics and open-source software at the Revolutions blog (, and was named a top 10 influencer on the topic of “Big Data” by Forbes. He is the co-author (with Bill Venables) of the tutorial manual, An Introduction to R, and one of the originating developers of the ESS: Emacs Speaks Statistics project. Prior to joining Revolution Analytics, David was the director of product management for S-PLUS at Insightful, Inc. Follow David on Twitter as @revodavid.


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