INTEGRATION OF UZBEK SENTIMENT ANALYSIS MODELS INTO REAL-WORLD SYSTEMS
Keywords:
Sentiment Analysis, Uzbek Language, System Integration, Microservices, MLOps, Model Drift, Docker, Kubernetes, Uzum Market Corpus, Asynchronous Pipelines, Data Privacy.Abstract
This paper outlines the comprehensive technical framework and operational methodologies required to integrate Uzbek-language sentiment analysis models into production environments. It details the current landscape of Uzbek sentiment corpora—including manually annotated social media data, translated review sets, and a large-scale e-commerce dataset from Uzum Market—which collectively establish the empirical foundation for robust model training. The study structures a multi-stage microservice integration pipeline using Python-based RESTful APIs (Flask/FastAPI) and Docker containerization, emphasizing architectural modularity. Furthermore, we contrast real-time versus batch inference modes, establish an MLOps perspective for monitoring model drift via EvidentlyAI alongside Prometheus/Grafana infrastructure stacks, and tackle language-specific deployment challenges such as script mixing and low-resource computational constraints. Finally, the paper addresses critical system isolation strategies, horizontal scaling through Kubernetes, and ethical imperatives regarding data privacy, PII anonymization, and informed consent in sensitive deployment domains.
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