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 SPAM 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 IngersollKarl Wettin Isabel DrostTed DunningJeff Eastman Dawid Weiss Otis Gospodnetic Erik Hatcher Sean Owen Ozgur Yilmazel

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

7. Example: K-Means Given K, assign the first K random points to be the initial cluster centers Assign subsequent points to the closest cluster using the supplied distance measure Compute the centroid of each cluster and iterate the previous step until the cluster centers converge within delta Run a final pass over the points to cluster them for output

8. K-Means Map/Reduce Design Driver – Runs multiple iteration jobs using mapper+combiner+reducer – Runs final clustering job using only mapper Mapper – Configure: Single file containing encoded Clusters – Input: File split containing encoded Vectors – Output: Vectors keyed by nearest cluster Combiner – Input: Vectors keyed by nearest cluster – Output: Cluster centroid vectors keyed by “cluster” Reducer (singleton) – Input: Cluster centroid vectors – Output: Single file containing Vectors keyed by cluster

9. K-Means Hadoop Implementation KMeansDriver – runJob() – runIteration() – isConverged() – runCluster() KMeansMapper – configure() – map() KMeansCombiner – configure() – reduce() KMeansReducer – configure() – reduce() Cluster configure() formatCluster() decodeCluster() addPoint() computeCentroid() accessors

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

11. GSoC @ Mahout Many interesting submissions 4 projects approved for Mahout (http://code.google.com/soc/2008/asf/about.html) – “Mahout: Parallel implementation of [NB/SOM/RF] 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

12. 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 – Access to clusters that we could use for testing