Abstract
Currently, there are a large number of articles describing the theoretical aspects of development in the field of machine learning. However, the experience of their practical application in real systems is described much less often. Basically, authors describe the efficiency, accuracy, and other performance metrics of the resulting solution, but everything stops at the prototype stage. At the same time, how the trained model will behave not on test data, but in real conditions, can be very different from the indicators obtained at the development stage. This article describes the experience of the implementation and real use of a classification service based on machine learning techniques.
Highlights
Scientific articles on the development of new, or improvement of already existing methods of machine learning are constantly published in journals and collections of articles released as a result of conferences
We can say that the system described in the article does not include a large number of implementation details, but at a general level it allows you to describe the structure of an ML-based application working in business, and this is the aim of this article
The described system has been successfully working for several years
Summary
Scientific articles on the development of new, or improvement of already existing methods of machine learning are constantly published in journals and collections of articles released as a result of conferences. Against the backdrop of a large amount of academic research, the number of articles describing the practice of using machine learning to solve real-world problems is not so large. This article describes the experience of using the classification model in a real working business system. The task was to create an internal service that would return a set of relevant categories based on some text description. At the beginning of the project, there was a database with a large number of texts and related to each category. As part of the task, the initial import and preparation of data for training were made, the classification model was trained, the effectiveness of several types of classification models was tested, a microservice was created and its load testing was performed
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