FEATURED ARTICLES

• Representative Samples For LSI: Why The Number Of Inspections Isn't What Matters Most

  Read how machine learning and artificial intelligence allow us to get much more out of the data we collect, finding subtle patterns, capturing complex nuances, and using these to build predictive models.

• When A Representative Sample Of Service Lines Reveals Zero Lead, What Can You Say?

  It’s easy to find evidence for a risk of lead exposure from water service lines. How do we show that there isn’t a meaningful risk of lead in our water system?

• Why We Use AWS For Our Machine Learning Platform At BlueConduit

  Properly deployed, predictive machine learning can bridge gaps, accelerating the work of modernizing our water systems in an explainable manner that makes the best use of our scarce resources.

• How To Identify And Address Bias In Your LSL Inventories

  Understand why it's so important that water systems can recognize and address sources of bias in their lead service line inventory process.

• Why Let A Computer Tell Me Where To Dig? Separating The Benefits Of Data Science From "AI" Snake Oil

  Learn about the importance of doing data science right, and applying the right machine learning approach to the right part of the job.

• How To Evaluate Predictive Models For Lead Service Line Inventory And Replacement

  This guide will cover what predictive modeling is and give you seven questions to ask to help you choose wisely.

• Five Reasons To Start Building Your LSL Inventory NOW

  With increased public awareness about the risk of lead in drinking water, water utilities are trying to be more transparent about pipe materials in their distribution system. Here are five reasons to build your LSL inventory sooner than later.

• How Artificial Intelligence Improves Efforts To Find And Replace Lead Service Lines

  This article explores why machine learning and artificial intelligence can be helpful in the efforts to replace lead drinking water service lines. 

• Helping Detroit Get The Lead Out

  Learn how the Kresge Foundation promotes efficient and equitable lead abatement efforts by supporting the use of BlueConduit's machine learning technology. 

• Flint Service Line Map, Powered By BlueConduit, Highlights Progress And Remaining Work

  BlueConduit is excited to announce the release of the Flint Service Line Map, an interactive tool that allows residents, policy makers, and advocacy groups to examine residential water service line materials and predictions in Flint, Michigan.

• Getting The Lead Out Of Flint Part 1: The Path To Data Science

  After discovering elevated levels of lead in Flint's drinking water, the city of Flint announced the FAST start program to replace the city's lead service lines. 

• EPA Service Line Inventory Guidance Includes Machine Learning Recommendations

  BlueConduit is proud to have their work cited in several sections of the EPA's Service Line Inventory Guidance. 

• Precision Vs. Recall For Predicting Service Line Materials

  Consider what makes a "good" prediction and the choice to be made between the prioritization of precision and recall.