Why We Use AWS For Our Machine Learning Platform At BlueConduit

By Abay Israel

Our public drinking water system requires investment and maintenance. There are hundreds of thousands of water main breaks each year and even when the system works well it loses billions of gallons to leaks annually. In most communities, sections of the water system are at least 100 years old—and likely include lead service lines. 

This problem is compounded by lack of trustworthy records. Service line records are typically scattered across multiple systems, unorganized, incomplete, often illegible, and frequently contradictory or incorrect. Nonetheless, those historical records hold valuable information for data science teams—if they can get at it. As it stands, creating a usable dataset from these records is a largely manual process, time-intensive, and difficult to accelerate in any meaningful way.

Unfortunately, water utilities are persistently underfunded and underserved. Most won’t have the resources to attract and retain the sort of data science talent that they need to efficiently track down lead contaminant sources in our water systems—let alone afford to waste analyst time puzzling over faded tap cards.