USING MLOPS FOR DEPLOYMENT OF OPINION MINING MODEL AS A SERVICE FOR SMART CITY APPLICATIONS

Aigerim Mussina, Didar Yedilkhan, Yermek Alimzhanov, Aliya Nugumanova, Sanzhar Aubakirov, Aigerim Mansurova

Scientific Journal of Astana IT University, 2025

Full text Open access: Gold Metadata (CSL-JSON)
DOI 10.37943/21cpqx5616
Data confidence 85 high
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Abstract

This paper presents the MLOps strategy, which adapts the automation principles of DevOps to the deployment and lifecycle management of artificial intelligence (AI) models. By leveraging high-performance automation, MLOps ensures seamless AI development and operations integration, enabling efficient and reliable model deployment. The study demonstrates this approach by implementing the Astana Opinion Mining macro-service customized for sentiment analysis. This macro-service evaluates public opinions based on a criteria taxonomy for assessing the urban environment's sustainable development. As a smart city application, the system facilitates the collection and analysis of citizen feedback to assess the performance of city services and inform urban planning decisions. Technologically, the MLOps strategy employs containers and microservices to construct robust data and process pipelines. Four core pipelines were developed in this research: data collection, feature engineering, experimentation, deployment, and maintenance. The data collection pipeline is achieved through automated crawling from diverse sources such as social media and other internet platforms. The feature engineering pipeline ensures data preprocessing by removing noise, identifying message languages, categorizing topics, and preparing data for further analysis. The experimentation pipeline incorporates services for data labeling, model training, and performance evaluation customized to sentiment analysis tasks. Finally, the deployment pipeline and maintenance pipeline deliver trained models to end-users, ensuring their continual improvement and adaptation. Using this MLOps framework, four models of sentiment analysis were tested in Russian: "Blanchefort," "Sismetanin," "MonoHime," and "Dostoevsky." The "Blanchefort" showed an accuracy of 71,43%. The resulting MLOps framework is fault-tolerant, scalable, and enables real-time urban environment assessments. By automating workflows, the architecture enhances operational efficiency, offering practical applications for smart city initiatives and sustainable urban development, contributing to better decision-making.