About This Architecture

AI/ML water quality monitoring pipeline combines IoT sensor data with physics-based kinetic models and machine learning to predict contamination levels and classify water safety in real time. Raw sensor streams from fluorescence, chemical, and environmental sources flow through a medallion architecture—bronze ingestion, silver processing with noise filtering and normalization, and gold AI/ML fusion—where a hybrid model merges Random Forest regression and classification with kinetic analysis. The system outputs CFU/mL contamination estimates, water quality classifications, and confidence scores, then routes alerts through a real-time notification engine to water authorities and mobile endpoints. MLOps governance via MLflow and Unity Catalog ensures reproducible model training, registry management, and compliance across the entire pipeline.

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How do you build an end-to-end machine learning pipeline for water quality monitoring that combines IoT sensors with physics-based models and real-time alerts?

This diagram shows a medallion-architecture ML pipeline where IoT fluorescence, chemical, and environmental sensors feed into a bronze ingestion layer, then flow through silver processing (noise filtering, normalization, missing value handling) and into a gold AI/ML stage. A hybrid model fuses Random Forest regression and classification with kinetic analysis to output CFU/mL contamination estimate