Mahout is an open source machine learning library from Apache. At the moment it means collaborative filtering / recommender engines, clustering, and classification.
Mahout aims to be the machine learning tool of choice when the data to be processed is very large, perhaps far too large for a single machine. In its current incarnation, these scalable implementations are written in Java, and some portions are built upon Apache’s Hadoop distributed computation project.
Mahout does not provide a user interface, a pre-packaged server, or installer. It is a framework of tools intended to be used and adapted by developers.
Robin is a Committer at the Apache Software Foundation where he works with the Mahout Machine Learning community. He is also a co-author of “Mahout in Action” by Manning Publications, a book on how Mahout is used to perform Machine learning on Terabytes of data with ease.
He used to be a Tech Lead on the ML infrastructure for Minekey Inc, a valley based startup which focussing on recommendations and behavioral targeting for publisher content. He was introduced to the newly born Mahout community through the Google Summer of Code program while he was a dual-degree student at IIT Kharagpur. Since then, he has been trying to model machine learning algorithms in to the Map/Reduce format and have successfully merged his Complementary Naive Bayes and Frequent Pattern Mining implementations with the Mahout code base. He is currently working as a Software Engineer at Google, Bangalore. He finds time from work to contribute actively to the Mahout community.
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