Presentation slide for bukatalks feat JVM Meet Up at Bukalapak

1. Disclaimer
Presentations are intended for educational
purposes only and do not replace independent
professional judgment. Statements of fact and
opinions expressed are those of the participants
individually and don’t necessarily reflect those of
blibli.com.
Blibli.com does not endorse or approve, and
assumes no responsibility for, the content,
accuracy or completeness of the information
presented.

2. Machine Learning Research in blibli
Hendri Karisma
hendri.karisma@gdn-commerce.com / hendri.k.id@ieee.org

3. Hendri Karisma
• Sr. Research and Development Engineer at
blibli.com (PT. Global Digital Niaga)
• Rnd Team for Data Science/intelligence system
• Working for Fraud Detection System. Current
working in dynamic recommendation system
project.

4. Definition of Informatics
“Automation of Information” –
Prof. Dr. Ing. Iping Supriana

5. Machine Learning Definition
“A computer program is said to learn from
experience E with respect to some class of tasks
T and performance measure P, if its performance
at tasks in T, as measured by P, improves with
experience E.” – Prof. Tom Mitchel

6. How it works

7. Machine Learning #1
• Supervised
• Unsupervised
• Reinforcement Learning
• Semi-Supervised
• Deep Learning

8. Machine Learning #2

9. Machine Learning Taxonomy

10. JVM Tech (tools) for ML
• weka
• Deeplearning4j (working with spark and GPU)
• H2O (working with spark and GPU, support
Tensorflow, MxNet, and cafe)
• jcuDNN (JNI for wrapping nVidia cuDNN)
• Mahout
• MLlib spark

11. Artificial Intelligence in Industry
• Fraud Detection System
• Dynamic Recommendation System and User
Profiling
• Traveling Salesman Problem and Binpacking
Problem for better warehouse management
• Social Media Analysis
• Chatbot
• Company condition forecasting
• Governance simulation

12. The Complexity #1

13. The Complexity #2
• Big data : volume, variety, velocity, and veracity. (You
might consider a fifth V, value.)
• Knowledge representation or the architecture of the
model
• Unimplemented methods/algorithms in any libraries
• Stack of methods
• Data mostly unlabeled data
• Data resources (microservices)
• Features Engineering (especially from unstructured
data)
• Machines (Hardware)
• High Performance Computing

14. Stack of Methods
• More complex methods and models
• Methods characteristic & behavior
• Methods customization
• Ex. Semi-supervised, Deep learning, features
engineering
• Sample cases : our research in FDS and
dynamic recommendation system

15. High Performance Computing
https://hadoop4usa.wordpress.com/2012/04/13/scale-out-up/

16. Machines
• Cluster machine
• GPU machines (OpenCL and nvidia CUDA)

17. High Performance Computing #2
• In-memory data fabric: provides low-latency access
and processing of large quantities of data by
distributing data across the dynamic random access
memory (DRAM), Flash, or SSD of a distributed
computer system.

18. Machine Learning Flow
Scala + Play + Akka + Mongo
The problem is microservices

19. Next Machine Learning Flow
Scala + Play +
Akka + Mongo +
kafka

20. New Engine Machine Learning
• Kafka
• Tensorflow (utilize GPU)
• Spark
• Java/Scala/python

21. Dynamic Collaborative Filtering using HMM

22. Topological Data Analysis
• Coordinate invariance
• Deformation Invariance
• Compressed Representations

23. Averi Project
AVERI PROJECT
twitter porn account detection

24. Example
AVERI PROJECT
twitter prostitution account detection

25. THANK YOU
Any question?