Round Table: Ted Dunning - Abuse a Search Engine to do Data Science (How to build a recommendation engine from common household items)
Pizza and sodas provided on a first-come first-served basis.
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Search engines are one of the most under-recognized tools in the data science armamentarium. Search engines can be used for a variety of high-value machine learning applications. This fact is not widely recognized, however.
Practical machine learning involves more than just machine learning itself. It is also necessary to be able to deploy, monitor, manage and understand the operation of any advanced analytics application. Recent developments in machine learning allow search engines such as Lucene and Solr to be used to deploy such advanced analytics systems. This has many practical benefits because search engines based on Lucene collectively have nearly a mega-year of run-time and are thus correspondingly stable.
Ted Dunning is Chief Applications Architect at MapR Technologies and committer and PMC member of the Apache Mahout, Apache ZooKeeper, and Apache Drill projects and mentor for Apache Storm, DataFu, Flink and Optiq projects. Ted was the chief architect behind the MusicMatch (now Yahoo Music) and Veoh recommendation systems. He built fraud detection systems for ID Analytics (LifeLock) and he has 24 patents issued to date and a dozen pending. Ted has a PhD in computing science from the University of Sheffield. When he’s not doing data science, he plays guitar and mandolin.