1. Apache Mahout Industrial Strength Machine Learning Jeff Eastman

2. Current Situation Large volumes of data are now available Platforms now exist to run computations over large datasets (Hadoop, HBase) Sophisticated analytics are needed to turn data into information people can use Active research community and proprietary implementations of “machine learning” algorithms The world needs scalable implementations of ML under open license - ASF

3. Where is ML Used Today Internet search clustering Knowledge management systems Social network mapping Taxonomy transformations Marketing analytics Recommendation systems Log analysis & event filtering Fraud detection

4. History of Mahout Summer 2007 – Developers needed scalable ML – Mailing list formed Community formed – Apache contributors – Academia & industry – Lots of initial interest Project formed under Apache Lucene – January 25, 2008

5. Who We Are (so far) Grant Ingersoll Karl Wettin Isabel DrostTed DunningJeff Eastman Dawid WeissOtis Gospodetnic Erik Hatcher

6. Current Code Base Matrix & Vector library – Hama collaboration for very large arrays Clustering – Canopy – K-Means – Mean Shift Utilities – Distance Measures – Parameters

7. Algorithms Under Development Naïve Bayes Perceptron PLSI/EM Taste Collaborative Filtering Integration Genetic Programming Dirichlet Process Clustering

8. GSoC @ Mahout Many interesting submissions 4 projects approved for Mahout (http://code.google.com/soc/2008/asf/about.html) – “Mahout: Parallel implementation of machine learning algorithms”, Farid Bourennani – “Implementing Logistic Regression in Mahout”, Yun Jiang – “Codename Mahout.GA for mahout-machine- learning”, Abdel Hakim Deneche – “To implement Complementary Naïve Bayes and Expectation Maximization algorithm using Map Reduce for Multicore Systems”, Robin Anil

9. Conclusion This is just the beginning High demand for scalable machine learning Contributors needed who have – Interest, enthusiasm & programming ability – Test driven development readiness – Comfort with the scary math (or bravery) – Interest and/or proficiency with Hadoop – Some large data sets you want to analyze