A quick introduction about Apache Mahout. A framework to develop machine learning solutions on top of hadoop

1. Jackson Oliveira
@cyber_jso

2. Data volume problems

3. To deal with them, we need to scale

4. Hadoop was built with these ideas in mind

5. Supported by an entire ecosystem

6. Mahout was built on top of hadoop.., The Elephant Rider

7. It was born as Lucene subproject… But It grew quickly
Became top level project on 2010

8. The main idea - A Java Library implementing ML Algorithms

9. Main Problem solving Areas - Collaborative Filtering

10. Main Problem solving Areas - Collaborative Filtering
Algorithm Single machine MR Spark
Item based
User Based
Matrix Factorization

11. Main Problem solving Areas - Clustering

12. Main Problem solving Areas - Clustering
Algorithm Single machine MR Spark
K-Means
Fuzzy K-Means
Streaming K-Means

13. Main Problem solving Areas - Classification

14. Main Problem solving Areas - Classification
Algorithm Single machine MR Spark
Naive Bayes/
Complementary Naive
Bayes
Random Forest
Multilayer Perception

15. Recommendations (CF) - User recommendation

16. Recommendations (CF) - The data model

17. Recommendations (CF) - Implementations

18. Who is using Mahout in production

19. Goods and bads
● Several algorithms implementations ready to use
● Well documented java API
● More robust when compared to weeka
● Startup overhead when compared to Spark MLIB
● API target for programmers rather than data scientists
● Extensible API

20. What comes next?

21. Jackson Oliveira
@cyber_jso
Thank you!