A real-time platform that ingests air-quality data from sensors/APIs, tracks ML experiments with MLflow, and visualizes key pollution metrics in Grafana via Prometheus. Predictive models trained on historical data forecast alert conditions, with automated retraining and Dockerized jobs running in a secure cloud environment. These platforms often offer features such:
• Live AQI/PM2.5/PM10/NO₂/O₃ charts, trends, and heatmaps in Grafana.
• MLflow tracking for params, metrics, artifacts, and model registry.
• Forecasting and threshold-based alerts (email/Slack/Webhook) for upcoming risk.
• Automated retraining and scheduled ETL pipelines on fresh data.
• Prometheus health checks, uptime monitoring, and latency metrics.
• Data cleaning, anomaly detection, and quality checks before model use.
• Secure, containerized deployment (Docker) with repeatable builds.
• Role-based access and REST endpoints for external integrations.
These platforms typically offer features like live AQI/PM2.5 dashboards, forecasting with threshold alerts, MLflow experiment tracking/model registry, automated retraining, Prometheus/Grafana observability, and role-based API access. By unifying data, models, and monitoring, the system enables faster decisions, collaborative analysis, and transparent reporting for teams and communities.