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<p>Pollution is a growing problem with dire effects on public health; therefore, accurate prediction of Air Quality Index (AQI) is paramount for both urban development and public health strategy. Here, we present GreenAirOps, a production-ready MLOps system that ingests multithsource environmental data, automatizes preprocessing and feature extraction, and combines Random For- est and XGBoost with ensemble learning for prediction of low-latency, near real-time AQI. This production pipeline has full MLOps pipeline capabilities: Data versioning with DVC; experiment tracking and model registration with MLflow; automated retraining and deployment using GitHub Actions. The deployment infrastructure utilizes Docker containers run on an AWS environ- ment and is designed for production grade. The specific contributions of this work are: an optimized ensemble learning system for low-latency AQI prediction; an end-to-end MLOps system which guarantee reproducibility and operational capability; and a set of production grade functions for auto-retraining, model health monitoring and auto-rollback.</p>

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GreenAirOps: production-ready MLOps for real-time air quality index prediction

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年8月4日

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  • Sahil Goyal,
  • Vaibhav Sharma,
  • Vivek Hotchandani,
  • Girish Mahale,
  • Mayur Gaikwad,
  • Aniket K. Shahade

摘要

Pollution is a growing problem with dire effects on public health; therefore, accurate prediction of Air Quality Index (AQI) is paramount for both urban development and public health strategy. Here, we present GreenAirOps, a production-ready MLOps system that ingests multithsource environmental data, automatizes preprocessing and feature extraction, and combines Random For- est and XGBoost with ensemble learning for prediction of low-latency, near real-time AQI. This production pipeline has full MLOps pipeline capabilities: Data versioning with DVC; experiment tracking and model registration with MLflow; automated retraining and deployment using GitHub Actions. The deployment infrastructure utilizes Docker containers run on an AWS environ- ment and is designed for production grade. The specific contributions of this work are: an optimized ensemble learning system for low-latency AQI prediction; an end-to-end MLOps system which guarantee reproducibility and operational capability; and a set of production grade functions for auto-retraining, model health monitoring and auto-rollback.