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Webinar: Predictive AI Forecasting and Environmental Monitoring to Prevent Asset Impact Events

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Webinar: Predictive AI Forecasting and Environmental Monitoring to Prevent Asset Impact Events

On May 17, 2018, JWN and Carl Solutions Data hosted a Webinar on Predictive Forecasting and Environmental Monitoring, and the exciting potential of using machine learning to forecast possible asset impact events up to seven days in advance. 

If you missed the webinar the first time around or would like to review the content for further detail please click WATCH VIDEO.


Event Speakers
Kevin Marsh, Vice President, Business Development, Carl Data Solutions

Carl Data’s VP of Business Development has over 30 years of experience in technical sales. As VP of Sales of Marsh-McBirney, he led one of the water monitoring industry’s first successful efforts to commercialize an IoT based Data-as-a-Service (DaaS) solution, transitioning that business model to Hach Company as the lead of their Data Delivery Services division. He then joined Software-as-a-Service (SaaS) start-up OptiRTC, Inc. as their Vice President of Sales and Marketing, to further pursue his passion for bringing disruptive technologies to an otherwise conservative marketplace. In addition, he has held numerous leadership positions in the Water Environment Federation and the American Water Works Association, and holds a BA in International Relations from the University of Delaware.

Piotr Stepinski, Chief Technology Officer, Carl Data Solutions

Carl Data’ CTO is a Senior Software Engineer with over a decade of Big Data application experience. His product development background allows him to lead projects with an agile and explorative development method that encourages innovation. He has experience applying software development and machine learning to a variety of industries, including utilities, health care and finance. He was Senior Software Engineer for Kainos Group and Atena & Telzas. Recently, he co-published a case study with Microsoft based on Carl Data’s break-through machine learning for sensor anomaly detection. He has an MSc in Electronics, Telecommunications and Informatics from Gdansk University of Technology.