Preventing Revenue Leakage and Monitoring Distributed Systems with Machine Learning Eiti Kimura and Flavio Clésio
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Have you imagined a simple machine learning solution able to prevent revenue leakage and monitor your distributed application? To answer this question, we offer a practical and a simple machine learning solution to create an intelligent monitoring application based on simple data analysis using Apache Spark MLlib. Our application uses linear regression models to make predictions and check if the platform is experiencing any operational problems that can impact in revenue losses. The application monitor distributed systems and provides notifications stating the problem detected, that way users can operate quickly to avoid serious problems which directly impact the company’s revenue and reduce the time for action. We will present an architecture for not only a monitoring system, but also an active actor for our outages recoveries. At the end of the presentation you will have access to our training program source code and you will be able to adapt and implement in your company. This solution already helped to prevent about US$3mi in losses last year.
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Preventing Revenue Leakage and Monitoring Distributed Systems with Machine Learning Eiti Kimura and Flavio Clésio
- 1. Flavio Clésio, Movile Eiti Kimura, Movile PREVENTING REVENUE LEAKAGE AND MONITORING DISTRIBUTED SYSTEMS WITH MACHINE LEARNING #EUai10
- 2. #EUai10 ABOUT US 2 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 (2016) flavioclesio
- 3. #EUai10 ABOUT US 3 • IT Coordinator and Software Architect at Movile • Msc. in Electrical Engineering • Apache Cassandra MVP (2014/2015 and 2015/2016) • Apache Cassandra Contributor (2015) • Cassandra Summit Speaker (2014 and 2015) • Strata Hadoop World Singapore Speaker (2016) Eiti Kimura eitikimura
- 4. Movile is the company behind several apps that makes the life easier WE MAKE LIFE BETTER THROUGH OUR APPS
- 5. #EUai10 Agenda 5 ● The Movile's Platform Case ● Practical Machine Learning Model Training ● Key Takeaways and Results
- 10. #EUai10 10
- 11. #EUai10 Main Problem: Monitoring 11 How can we check if platform is fully functional based on data analysis only? Tip: what if we ask help to an intelligent system?
- 12. #EUai10 The Data Volumetry 12 ● 236 Millions + of billing requests attempt a day ● 4 main mobile carriers drive the operational work
- 13. #EUai10 Stating the problem 13 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
- 14. #EUai10 The Modeling Lifecycle 14 Training Data Testing Data Feature Extraction Train Score Model Evaluation Dataset
- 15. #EUai10 15
- 17. #EUai10 Evaluating Model Results 17 Machine Learning Tested Model Accuracy RMSE Lasso with SGD Model 35% 0.32 Ridge Regression with SGD Model 87.5% 0.13 Elastic Net with SGD Model 35% 0.32 Decision Tree Model 93.4% 0.05
- 18. #EUai10 Watcher-ai Introduction 18 Hi I'm Watcher-ai! It is nice to see you here Applied Machine Learning to solving problems
- 20. #EUai10 20 Watcher-ai using models
- 21. #EUai10 21 Watcher-ai request predictions
- 24. Lessons Learned Empirical observations about this kind of problem
- 25. #EUai10 • Regularization doesn't fit so well with our low dimensional data • Linear Methods are good for extrapolation but Decision Trees are more suitable for interpolation problems Regularization and Linear Methods 25
- 26. #EUai10 • Time Series with thresholds didn't work in the past because we have several exogenous factors that make the regular algorithms behaving badly. • We avoid (totally removed) fixed thresholds based on standard deviations The Timeseries Thing 26
- 27. #EUai10 Why we changed from RDD to Dataframe? 27 RDD (2011) DataFrame (2013) distributed collection of JVM objects functional operators like (map, filter, etc) Distributed collection of Row objects Expression-base operations and UDF Logical plans and optimizer Fast/efficient internal representation
- 28. #EUai10 Why we changed from RDD to Dataframe? • A good way to perform Grid-Search in our models • Simpler and cleaner code, better to debug 28
- 29. Final Results
- 30. #EUai10 30 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
- 31. #EUai10 • Able to prevent revenue loss • The main monitoring system • Successful case of applied Machine Learning • Simple solution with Apache Spark Our Goals 31