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:
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.
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 (blog.revolutionanalytics.com), 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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