1) Apache Mahout is a scalable machine learning library built on Hadoop that allows for distributed machine learning algorithms and techniques. 2) It aims to overcome limitations of other open source machine learning libraries by providing scalability, documentation, examples, and an Apache license. 3) Current capabilities include basic linear algebra, collaborative filtering for recommendations, clustering algorithms like k-means, and naive Bayes classification. More algorithms are planned for future releases.

1. Introducing Apache Mahout
Scalable Machine Learning for All!
Grant Ingersoll

2. Agenda
• What is Machine Learning?
– Definitions
– Types
– Applications
• Mahout
– What?
– Why?
– How?
– Who?

3. What is Machine Learning?
NOT!
QuickTimeª and a
decompressor QuickTimeª and a
are needed to see this picture. Or? decompressor
are needed to see this picture.
http://en.wikipedia.org/wiki/Image:Hal-9000.jpg
http://upload.wikimedia.org/wikipedia/en/4/49/Terminator.jpg

4. How about?
Google News

5. Or?
Amazon.com

6. Definition
• “Machine Learning is programming
computers to optimize a performance
criterion using example data or past
experience”
– Intro. To Machine Learning by E.
Alpaydin
• Subset of Artificial Intelligence
– Many other fields: comp sci., biology,
math, psychology, etc.

7. Characterizations
• Lots of Data
• Identifiable Features in that Data
• Too big/costly for people to handle
– People still can help

8. Types
• Supervised
– Using labeled training data, create
function that predicts output of unseen
inputs
• Unsupervised
– Using unlabeled data, create function
that predicts output
• Semi-Supervised
– Uses labeled and unlabeled data

9. Classification/Categorization
• Spam Filtering
• Named Entity Recognition
• Phrase Identification
• Sentiment Analysis
• Classification into a Taxonomy

10. Clustering
• Find Natural Groupings
– Documents
– Search Results
– People
– Genetic traits in groups
– Many, many more uses

11. Collaborative Filtering
• Recommend people and products
– User-User
• User likes X, you might too
– Item-Item
• People who bought X also bought Y

12. Info. Retrieval
• Learning Ranking Functions
• Learning Spelling Corrections
• User Click Analysis and Tracking

13. Other
• Image Analysis
• Robotics
• Games
• Higher level natural language
processing
• Many, many others

14. What is Apache Mahout?
• A Mahout is an elephant
trainer/driver/keeper, hence…
QuickTimeª and a
decompressor
are needed to see this picture.
+ (and other distributed techniques)
Machine Learning
=

15. What?
• Hadoop brings:
– Map/Reduce API
– HDFS
– In other words, scalability and fault-
tolerance
• Thus, Mahout’s Goal is:
– Scalable Machine Learning with Apache
License

16. Why Mahout?
• Many Open Source ML libraries either:
– Lack Community
– Lack Documentation and Examples
– Lack Scalability
– Lack the Apache License ;-)
– Or are research-oriented
• Personal: Learn more ML
• Intelligent Apps are the Present and Future
– See the Hadoop talks tomorrow and Friday!
• Goal: Overcome gaps the Apache Way!

17. Current Status
• Close to Initial release
– Focused on examples, docs, bug fixes
• What’s in it:
– Simple Matrix/Vector library
– Taste Collaborative Filtering
– Clustering
• Canopy/K-Means/Fuzzy K-Means/Mean-shift
– Classifiers
• Naïve Bayes
• Complementary NB
– Evolutionary
• Integration with Watchmaker for fitness function

18. How?
• Examples
– Taste
– Clustering
– Classification
– Evolutionary

19. Taste: Movie
Recommendations
• Given ratings by users of movies,
recommend other movies
• http://lucene.apache.org/mahout/taste
.html#demo

20. Clustering: Synthetic Control
Data
• http://archive.ics.uci.edu/ml/datasets/Synthetic+
• Each clustering impl. has an example
Job for running in
<MAHOUT_HOME>/examples
– o.a.mahout.clustering.syntheticcontrol.*
• Outputs clusters…

21. Classification: NB and CNB
Examples
• 20 Newsgroups
– http://cwiki.apache.org/confluence/display/MA
• Wikipedia
– http://cwiki.apache.org/confluence/display/MA

22. Evolutionary
• Traveling Salesman
– http://cwiki.apache.org/confluence/displa
y/MAHOUT/Traveling+Salesman
• Class Discovery
– http://cwiki.apache.org/confluence/displa
y/MAHOUT/Class+Discovery

23. What’s Next?
• Release 0.1!
• Shared Amazon Images (others?)
• More Examples
• Winnow/Perceptron (MAHOUT-85)
• Hbase and HAMA support
• Normalize I/O format for data
• Solr Integration (SOLR-769)
• Other Algorithms: SVM, Linear Regression,
etc.

24. When, Where, Who
• When? Now!
– Mahout is growing
• Who? You!
– We want Java programmers who:
• Are comfortable with math
• Like to work on large, hard problems
• Where?
– http://lucene.apache.org/mahout
– http://cwiki.apache.org/MAHOUT
– mahout-{user|dev}@lucene.apache.org

25. Resources
• “Programming Collective Intelligence”
by Toby Segaran
• “Data Mining - Practical Machine
Learning Tools and Techniques” by
Ian H. Witten and Eibe Frank
• Hadoop - http://hadoop.apache.org
• http://mloss.org/software/