Ted Dunning and Ellen Friedman
Ted Dunning is Chief Applications Architect at MapR Technologies and active in the open source community, serving as a member of the board of the Apache Software Foundation. He recently designed the t- digest algorithm used in several open source projects.
Previously, Dunning was the chief architect behind the MusicMatch (now Yahoo Music) and Veoh recommendation systems, built fraud-detection systems for ID Analytics (LifeLock), and has 24 issued patents to date. He has a PhD in computing science from University of Sheffield. When he’s not doing data science, he plays guitar and mandolin. Dunning is on Twitter as @ted_dunning.
Ellen Friedman is principal technologist for MapR, a scientist, and O'Reilly author currently writing about a variety of open source and big data topics. Friedman is a committer on the Apache Drill and Mahout projects, and she has a PhD in biochemistry. She is coauthor of a number of short data books published by O’Reilly Media, including Machine Learning Logistics, Streaming Architecture, the Practical Machine Learning series, Time Series Databases, and Introduction to Apache Flink. She has been an invited speaker at Strata + Hadoop in London, Berlin Buzzwords, Nike Tech Talks, the University of Sheffield Methods Institute, and Philly ETE as well as a keynote speaker for Big Data London and for NoSQL Matters in Barcelona.
Articles by Ted Dunning and Ellen Friedman
There's a surprising trick for greatly increasing the chances of real impact, true success with many types of machine learning systems, and that is "do the logistics correctly and efficiently." That sounds like simple advice - it is - but the impact can be enormous. If the logistics are not handled well, machine learning projects generally fail to deliver practical value. In fact, they may fail to deliver at all. But carrying out this advice may not seem simple at all.
Posted October 26, 2017