Recommendation Systems, though commonly seen, are among the challenging aspects of Predictive Analytics and Big Data Platforms/Solutions. Various Algorithms are used to build the products to which Recommendation Systems are applied, and there is always buzz in regard to the various schemes employed.
This session brings more science to the art, laying the foundation of starting off a Recommendation Platform at a higher ground rather than from scratch. We’ll discuss the ML algorithms that should be applied to various use cases, and the architecture of a Recommendation Platform on Hadoop—including ETL/Data Pipeline, Feature Generation, Model Generation, Recommendation Server, A/B testing, Reporting and Tracking. We’ll also review the various patterns of recommendation use cases and the ML algorithms that apply to them.
Jayant is Solutions Architect at Cloudera working with various large and small companies in various Verticals on their Big Data Use Cases, Architecture, Algorithms and Deployments. Prior to Cloudera Jayant also worked at Yahoo where he was instrumental in building out the large scale Content/Listings Platform using Hadoop & Big Data technologies and working with various Yahoo Properties, Real Estate, Autos, Local, News, Movies etc. Prior to Yahoo, Jayant worked at eBay building out a new Shopping Platform (K2) using Nutch/Hadoop, Search Intelligence Platform, among others. Jayant also worked at KLA-Tencor building software for Reticle Inspection Stations and defect analysis systems. Jayant has Bachelor’s degree in Computer Science from IIT Kharagpur and Master’s degree in Computer Engineering from San Jose State University
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