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.

Automatic term extraction using deep learning models has shown promising results when sufficient training data is available. Unfortunately, in certain emerging domains and low-resource languages such data may be lacking, leading to reduced performance due to insufficient fine-tuning of these models. To address this issue, in this study we propose a multilingual transformer-based sequence labeling model for term extraction, trained on an original dataset in English and Kazakh. Our evaluation results demonstrate that the multilingual XLM-RoBERTa model achieves improvements in automatic term extraction compared to a model with BERT-BiLSTM-CRF architecture trained on limited training data. Although the degree of improvement varies across domains, languages and dataset sizes, our evaluation shows that in Kazakh subsets of the dataset the highest F1-score improvement reaches 20 % in the Blockchain domain, while in the domain of Materials Science the improvement amounts to 12.9 %. Furthermore, our multilingual model attains competitive results when compared to a model with popular and highly effective architecture trained on a small amount of training data.