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Practical Machine Learning Models to Prevent Revenue Loss — Eiti Kimura and Flávio Clésio (Movile)

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.


Eiti is an IT coordinator and architect of distributed and high-performance platforms at Movile Brazil. He has over 15 years of experience working with software development. Eiti is an enthusiast of open technologies — he was an Apache Cassandra MVP from 2014 to 2015 — and had vast experience with backend systems for carrier billing services, sending bulk text messages (SMS), and user action tracking. Eiti hold a master’s degree in electrical engineering with a specialization in software engineering.


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Flavio Clesio is a specialist in machine learning and revenue assurance at Movile, where he helps to develop core intelligent applications to exploit revenue opportunities and automation in decision making. Prior to Movile, Flavio was a business intelligence consultant in financial markets, specifically in nonperforming loans. He holds a master’s degree in computational intelligence applied in financial markets.