Machine learning model serving system for event streams

Aleksei Starikov, Dmitry Namiot


The paper considers the existing systems of streaming event processing, analyzes the characteristics of these systems, analyzes their advantages and disadvantages. Stream processing significantly reduces the time from the moment data is received to the reaction to it compared to batch processing. Using this approach, companies can respond to incoming information in a timely manner, taking action when they are needed. The paper presents an analysis of the systems used in large companies to develop machine learning models, from data collection to putting the model into operation. The paper discusses the issues of introducing machine learning models into a productive environment for streaming processing applications, including the presentation formats of models, and compares the advantages and disadvantages of various approaches. The architecture and practical are given and the practical implementation of the application is developed, which allows using each of the considered formats.

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