Nowadays with high data volumes, there’s a need to develop intelligent systems that can assist in data analysis and decision making. We offer a practical demonstration of machine learning to create an intelligent application based on distributed system data. We'll show machine learning techniques in the development of a data analysis application to monitor distributed platforms with direct impact on company revenue, saving more than 3M dollars a year. Also, we will provide a source code of a practical demonstration on how to train machine learning models and perform predictions with Apache Spark.

1. Eiti Kimura / Flávio Clésio
@Movile Brazil, June
PRACTICAL MACHINE LEARNING MODELS TO
PREVENT REVENUE LOSS
PAPIS.io Connect 2017

2. ABOUT US
Flávio Clésio
• Core Machine Learning at Movile
• MSc. in Production Engineering (Machine Learning in Credit Derivatives/NPL)
• Specialist in Database Engineering and Business Intelligence
• Blogger at Mineração de Dados (Data Mining) - http://mineracaodedados.wordpress.com
• Strata Hadoop World Singapore Speaker
flavioclesio

3. ABOUT US
• Software Architect and TI Coordinator at Movile
• Msc. in Electrical Engineering
• Apache Cassandra MVP (2014/2015 e 2015/2016)
• Apache Cassandra Contributor (2015)
• Cassandra Summit Speaker (2014 e 2015)
• Strata Hadoop World Singapore Speaker
Eiti Kimura
eitikimura

4. Movile is the company behind of several
apps that makes the life easier
WE MAKE LIFE BETTER
THROUGH OUR APPS

5. ● The Movile's Platform Case
● Practical Machine Learning Model Training
● Results and Conclusions
AGENDA

6. MOVILE'S SUBSCRIPTION
AND BILLING PLATFORM IN ITS SIMPLEST FORM
● A distributed platform
● User's subscription management
● MISSION CRITICAL platform: can not stop under any circumstance

7. REVENUE LEAKAGE LOSSES IMPACT ALONG THE DAY
● 1 Hour Outage: Dozens of thousands of BRL
● 3 Hour Outage: Some hundreds of thousands of BRL
● 12 Hour Outage: Millions of BRL

8. How can we check if platform is fully functional
based on data analysis only?
MAIN PROBLEM: MONITORING
Tip: what if we ask help to an intelligent system?

9. ● 230 Millions + of billing requests attempt a day
● 4 main mobile carriers drive the operational work
HOW DATA LOOKS LIKE?

10. DATA AND ALGORITHM MODELING
Sample of data (predicting the number of success)
featureslabel/target
# success carrier_weight hour week response_time #no_credit #errors # attempts
61.083, [4.0, 17h, 3.0, 1259.0, 24.751.650, 2.193.67, 26.314.551]
SUPERVISED LEARNING
Linear Regression

11. ● Linear Model with Stochastic Gradient (SDG)
● Lasso with SGD Model (L1 Regularization)
● Ridge Regression with SGD Model (L2 Regularization)
● Decision Tree with Regression Properties (Not CART)
TESTED ALGORITHMS USING SPARK MLLIB

12. MODELING LIFECYCLE
Training Data
Testing Data
Feature
Extraction
Train
Score
Model
Evaluation
Dataset

13. http://spark-notebook.io/
SPARK NOTEBOOK

14. Machine Learning Tested Model Accuracy RMSE
Linear Model With SGD 44% 1.64
Lasso with SGD Model 43% 1.65
Ridge Regression with SGD Model 43% 1.66
Decision Tree Model 96% 0.20
TIME TO EVALUATE THE RESULTS

15. WATCHER-AI INTRODUCTION
Hi I'm Watcher-ai!
It is nice to see you here

16. WATCHER-AI ARCHITECTURE

17. Avoid to Lose more than
U$ 3 Million Dollars preventing leakage
Saved more than 500
working hours
Recovery Time drops from
6 hours to 1 hour

18. • Automatic refeed and training using collected data. Analyse more data to predict
possible errors with carrier
• Notify more people and specific teams (more complex problems)
• Refactory to be more generic, so other teams can add their own algorithm
• Why not interact with Watcher to guide the analysis?
• Don't limit your mind, there is a lot to keep improving...
IT IS JUST A WARM UP

19. THANK YOU!
@eitikimura eiti-kimura-movile eiti.kimura@movile.com
@flavioclesio fclesio flavio.clesio@movile.com
github.com/fclesio/watcher-ai-samples
papis-2017-brazil