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Practical Machine Learning Models to Prevent Revenue Loss — Eiti Kimura and Flávio Clésio (Movile)
Jun
21
3:00 PM15:00

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. Eiti 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. He holds a master’s degree in electrical engineering with a specialization in software engineering.

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.

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Shortening the time from analysis to deployment with ML-as-a-Service — Luiz Andrade (TEVEC Sistemas SA)
Jun
21
2:30 PM14:30

Shortening the time from analysis to deployment with ML-as-a-Service — Luiz Andrade (TEVEC Sistemas SA)

The daily job of a Data Scientist ranges from a variety of tasks: improving models performance or dealing with framework structure implementations. Machine Learning as a Service, a hot topic in the field, implies thinking about architecture to allow constant improvements in performance for our products. This presentation shows one architecture design using RESTful resources, document-oriented databases and pre-trained pipelines to achieve real-time predictions of time series with high availability, scalability and freedom to Data Scientists work directly on improving the accuracy rate of our products. We fine tuned to work on time series forecasting which is a very challenging field that still needs better solutions in terms of innovative modeling. During the presentation will be shown how these decisions keep our Data Scientists focused on working with real data and thinking about improvements that can reach a large volume of time series instead of singular and localized actions.

Luiz has worked as COO in TEVEC since its foundation, promoting Machine Learning software development and implementation. He did Civil Engineering from Polytechnic School of the University of São Paulo, MSc Transportation Engineer from Polytechnic School of the University of São Paulo, Graduated in Global Supply Chain and Logistics from Massachusetts Institute of Technology.

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